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JosephZ
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vg150_val_sgg_prompt

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xet
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3
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stringlengths
1
7
image
imagewidth (px)
173
1.28k
prompt_open
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867 values
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867 values
objects
stringlengths
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10.2k
1
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [0, 0, 799, 557]}, {"id": "sidewalk.2", "bbox": [75, 306, 798, 596]}, {"id": "building.3", "bbox": [0, 0, 222, 538]}, {"id": "street.4", "bbox": [437, 280, 796, 539]}, {"id": "clock.5", "bbox": [419, 60, 496, 424]}, {"id": "window.6", "bbox": [600, 0, 799, 147]}, {"id": "man.7", "bbox": [369, 262, 446, 512]}, {"id": "man.8", "bbox": [236, 246, 296, 508]}, {"id": "sign.9", "bbox": [121, 11, 200, 191]}, {"id": "car.10", "bbox": [717, 340, 797, 504]}, {"id": "shirt.11", "bbox": [364, 293, 446, 393]}, {"id": "car.12", "bbox": [476, 317, 554, 413]}, {"id": "pant.13", "bbox": [386, 367, 435, 496]}, {"id": "shirt.14", "bbox": [239, 285, 294, 389]}, {"id": "pant.15", "bbox": [242, 382, 287, 501]}, {"id": "shoe.16", "bbox": [386, 483, 435, 511]}, {"id": "arm.17", "bbox": [367, 282, 398, 324]}, {"id": "bike.18", "bbox": [335, 317, 362, 353]}, {"id": "bike.19", "bbox": [318, 308, 346, 350]}, {"id": "glass.20", "bbox": [446, 315, 490, 339]}, {"id": "street.21", "bbox": [75, 326, 789, 589]}, {"id": "sneaker.22", "bbox": [240, 487, 292, 514]}, {"id": "bike.23", "bbox": [319, 317, 360, 352]}]
[{"subject": "man.8", "predicate": "wears", "object": "sneaker.22"}, {"subject": "sign.9", "predicate": "on", "object": "building.3"}, {"subject": "man.8", "predicate": "has", "object": "shirt.14"}, {"subject": "sidewalk.2", "predicate": "near", "object": "street.4"}, {"subject": "man.8", "predicate": "has", "object": "glass.20"}, {"subject": "man.8", "predicate": "wears", "object": "sneaker.22"}, {"subject": "man.7", "predicate": "has", "object": "shoe.16"}, {"subject": "man.8", "predicate": "has", "object": "shirt.14"}, {"subject": "man.8", "predicate": "wears", "object": "pant.15"}, {"subject": "man.7", "predicate": "has", "object": "shirt.11"}, {"subject": "man.7", "predicate": "has", "object": "pant.13"}, {"subject": "bike.19", "predicate": "parked on", "object": "sidewalk.2"}, {"subject": "bike.18", "predicate": "parked on", "object": "sidewalk.2"}, {"subject": "car.10", "predicate": "parked on", "object": "street.4"}, {"subject": "bike.18", "predicate": "on", "object": "sidewalk.2"}, {"subject": "man.8", "predicate": "wearing", "object": "shirt.14"}, {"subject": "bike.23", "predicate": "near", "object": "tree.1"}, {"subject": "man.7", "predicate": "wearing", "object": "shoe.16"}, {"subject": "bike.23", "predicate": "near", "object": "tree.1"}, {"subject": "shirt.14", "predicate": "on", "object": "man.8"}, {"subject": "man.8", "predicate": "wearing", "object": "glass.20"}, {"subject": "man.8", "predicate": "in", "object": "shirt.14"}, {"subject": "man.8", "predicate": "wearing", "object": "pant.15"}, {"subject": "tree.1", "predicate": "near", "object": "street.4"}, {"subject": "man.8", "predicate": "wearing", "object": "glass.20"}, {"subject": "bike.23", "predicate": "behind", "object": "man.8"}, {"subject": "tree.1", "predicate": "near", "object": "sidewalk.2"}, {"subject": "man.7", "predicate": "wearing", "object": "shirt.11"}, {"subject": "building.3", "predicate": "with", "object": "window.6"}]
2
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "sidewalk.1", "bbox": [318, 345, 796, 598]}, {"id": "building.2", "bbox": [567, 0, 796, 414]}, {"id": "building.3", "bbox": [168, 0, 427, 319]}, {"id": "man.4", "bbox": [319, 322, 461, 568]}, {"id": "pole.5", "bbox": [421, 0, 470, 533]}, {"id": "window.6", "bbox": [646, 66, 741, 232]}, {"id": "car.7", "bbox": [237, 351, 364, 460]}, {"id": "tree.8", "bbox": [0, 144, 89, 364]}, {"id": "tree.9", "bbox": [100, 192, 207, 354]}, {"id": "tree.10", "bbox": [56, 153, 118, 358]}, {"id": "window.11", "bbox": [642, 260, 741, 371]}, {"id": "window.12", "bbox": [744, 68, 797, 230]}, {"id": "car.13", "bbox": [348, 335, 429, 396]}, {"id": "window.14", "bbox": [747, 261, 796, 368]}, {"id": "window.15", "bbox": [629, 71, 648, 242]}, {"id": "bike.16", "bbox": [415, 403, 463, 466]}]
[{"subject": "building.2", "predicate": "has", "object": "window.6"}, {"subject": "building.2", "predicate": "has", "object": "window.12"}, {"subject": "building.2", "predicate": "has", "object": "window.11"}, {"subject": "building.2", "predicate": "has", "object": "window.14"}, {"subject": "building.2", "predicate": "has", "object": "window.15"}, {"subject": "bike.16", "predicate": "near", "object": "car.7"}]
3
Generate a structured scene graph for an image of size (640 x 480) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (640 x 480) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [128, 230, 636, 474]}, {"id": "girl.2", "bbox": [523, 93, 636, 231]}, {"id": "bag.3", "bbox": [572, 272, 638, 402]}, {"id": "hair.4", "bbox": [582, 96, 638, 205]}, {"id": "drawer.5", "bbox": [1, 421, 131, 465]}, {"id": "handle.6", "bbox": [16, 355, 90, 388]}, {"id": "desk.7", "bbox": [530, 158, 598, 215]}, {"id": "drawer.8", "bbox": [0, 319, 128, 442]}]
[{"subject": "girl.2", "predicate": "has", "object": "hair.4"}, {"subject": "bag.3", "predicate": "on", "object": "table.1"}, {"subject": "girl.2", "predicate": "has", "object": "hair.4"}, {"subject": "girl.2", "predicate": "with", "object": "hair.4"}, {"subject": "girl.2", "predicate": "with", "object": "hair.4"}, {"subject": "drawer.8", "predicate": "has", "object": "handle.6"}]
4
Generate a structured scene graph for an image of size (640 x 480) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (640 x 480) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "curtain.1", "bbox": [363, 0, 640, 300]}, {"id": "window.2", "bbox": [483, 0, 637, 305]}, {"id": "table.3", "bbox": [411, 239, 639, 432]}, {"id": "door.4", "bbox": [365, 0, 485, 334]}, {"id": "chair.5", "bbox": [387, 210, 480, 383]}, {"id": "pillow.6", "bbox": [181, 221, 289, 305]}, {"id": "desk.7", "bbox": [0, 341, 63, 479]}, {"id": "lamp.8", "bbox": [214, 63, 263, 211]}, {"id": "box.9", "bbox": [458, 164, 511, 187]}]
[{"subject": "chair.5", "predicate": "near", "object": "table.3"}]
5
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 799, 599]}, {"id": "shelf.2", "bbox": [432, 132, 798, 226]}, {"id": "book.3", "bbox": [432, 157, 453, 221]}, {"id": "chair.4", "bbox": [142, 357, 316, 596]}, {"id": "chair.5", "bbox": [375, 350, 541, 599]}, {"id": "window.6", "bbox": [35, 42, 131, 427]}, {"id": "desk.7", "bbox": [125, 360, 439, 463]}, {"id": "door.8", "bbox": [0, 5, 34, 529]}, {"id": "railing.9", "bbox": [46, 328, 128, 425]}, {"id": "light.10", "bbox": [68, 0, 182, 39]}, {"id": "paper.11", "bbox": [632, 466, 670, 532]}, {"id": "chair.12", "bbox": [139, 350, 547, 592]}]
[{"subject": "book.3", "predicate": "on", "object": "shelf.2"}, {"subject": "book.3", "predicate": "on", "object": "shelf.2"}, {"subject": "chair.4", "predicate": "near", "object": "desk.7"}, {"subject": "book.3", "predicate": "on", "object": "shelf.2"}]
6
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "counter.1", "bbox": [0, 8, 799, 532]}, {"id": "chair.2", "bbox": [0, 0, 300, 521]}, {"id": "bottle.3", "bbox": [474, 75, 596, 382]}, {"id": "plate.4", "bbox": [35, 100, 274, 213]}, {"id": "bowl.5", "bbox": [392, 0, 605, 127]}, {"id": "bag.6", "bbox": [626, 99, 771, 257]}, {"id": "bag.7", "bbox": [347, 46, 481, 181]}, {"id": "leg.8", "bbox": [135, 309, 217, 512]}, {"id": "logo.9", "bbox": [482, 254, 590, 367]}, {"id": "food.10", "bbox": [351, 100, 468, 178]}, {"id": "leg.11", "bbox": [4, 265, 60, 396]}, {"id": "glass.12", "bbox": [190, 0, 250, 98]}, {"id": "glass.13", "bbox": [272, 0, 327, 78]}, {"id": "fork.14", "bbox": [108, 135, 165, 209]}]
[{"subject": "fork.14", "predicate": "on", "object": "plate.4"}, {"subject": "chair.2", "predicate": "near", "object": "counter.1"}, {"subject": "chair.2", "predicate": "against", "object": "counter.1"}, {"subject": "bag.6", "predicate": "on", "object": "counter.1"}, {"subject": "bowl.5", "predicate": "on", "object": "counter.1"}, {"subject": "food.10", "predicate": "in", "object": "bag.7"}, {"subject": "bag.7", "predicate": "on", "object": "counter.1"}, {"subject": "food.10", "predicate": "in", "object": "bag.7"}, {"subject": "glass.13", "predicate": "on", "object": "counter.1"}, {"subject": "glass.12", "predicate": "on", "object": "counter.1"}, {"subject": "food.10", "predicate": "in", "object": "bag.7"}, {"subject": "fork.14", "predicate": "on", "object": "plate.4"}, {"subject": "glass.13", "predicate": "on", "object": "counter.1"}, {"subject": "glass.12", "predicate": "on", "object": "counter.1"}, {"subject": "fork.14", "predicate": "on", "object": "plate.4"}, {"subject": "fork.14", "predicate": "on", "object": "plate.4"}, {"subject": "leg.11", "predicate": "under", "object": "counter.1"}, {"subject": "chair.2", "predicate": "has", "object": "leg.8"}, {"subject": "chair.2", "predicate": "has", "object": "leg.11"}, {"subject": "bottle.3", "predicate": "has", "object": "logo.9"}, {"subject": "bag.7", "predicate": "of", "object": "food.10"}]
7
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "window.1", "bbox": [486, 0, 796, 391]}, {"id": "woman.2", "bbox": [0, 311, 310, 596]}, {"id": "desk.3", "bbox": [71, 356, 667, 512]}, {"id": "book.4", "bbox": [425, 417, 616, 514]}, {"id": "desk.5", "bbox": [578, 453, 797, 597]}, {"id": "laptop.6", "bbox": [588, 478, 764, 561]}, {"id": "chair.7", "bbox": [0, 536, 319, 596]}, {"id": "paper.8", "bbox": [60, 343, 168, 409]}, {"id": "book.9", "bbox": [86, 412, 181, 457]}, {"id": "pant.10", "bbox": [87, 552, 310, 591]}, {"id": "book.11", "bbox": [704, 531, 767, 571]}, {"id": "hair.12", "bbox": [0, 312, 39, 393]}, {"id": "lady.13", "bbox": [3, 314, 148, 583]}]
[{"subject": "book.11", "predicate": "on", "object": "desk.5"}, {"subject": "lady.13", "predicate": "with", "object": "hair.12"}, {"subject": "woman.2", "predicate": "wearing", "object": "pant.10"}, {"subject": "laptop.6", "predicate": "above", "object": "desk.5"}, {"subject": "woman.2", "predicate": "sitting on", "object": "chair.7"}]
8
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [12, 386, 565, 599]}, {"id": "man.2", "bbox": [111, 286, 377, 599]}, {"id": "shelf.3", "bbox": [0, 0, 222, 238]}, {"id": "shirt.4", "bbox": [111, 373, 335, 598]}, {"id": "desk.5", "bbox": [580, 428, 799, 592]}, {"id": "paper.6", "bbox": [582, 315, 763, 436]}, {"id": "chair.7", "bbox": [14, 478, 145, 599]}, {"id": "drawer.8", "bbox": [362, 493, 510, 582]}, {"id": "book.9", "bbox": [121, 39, 135, 133]}, {"id": "pant.10", "bbox": [188, 529, 364, 598]}, {"id": "drawer.11", "bbox": [603, 545, 789, 598]}, {"id": "shelf.12", "bbox": [0, 213, 207, 239]}, {"id": "paper.13", "bbox": [668, 289, 800, 338]}, {"id": "book.14", "bbox": [89, 158, 138, 217]}, {"id": "paper.15", "bbox": [475, 334, 531, 403]}, {"id": "hand.16", "bbox": [335, 402, 378, 436]}, {"id": "bag.17", "bbox": [702, 289, 778, 305]}, {"id": "cup.18", "bbox": [140, 166, 165, 216]}, {"id": "cup.19", "bbox": [444, 396, 496, 471]}]
[{"subject": "cup.19", "predicate": "on", "object": "desk.1"}, {"subject": "cup.19", "predicate": "on", "object": "desk.1"}, {"subject": "man.2", "predicate": "wears", "object": "shirt.4"}, {"subject": "bag.17", "predicate": "on", "object": "paper.13"}, {"subject": "drawer.8", "predicate": "of", "object": "desk.1"}, {"subject": "cup.18", "predicate": "on", "object": "shelf.12"}, {"subject": "paper.13", "predicate": "on", "object": "desk.5"}, {"subject": "man.2", "predicate": "has", "object": "hand.16"}, {"subject": "book.9", "predicate": "on", "object": "shelf.12"}, {"subject": "man.2", "predicate": "has", "object": "hand.16"}, {"subject": "cup.19", "predicate": "on", "object": "desk.1"}, {"subject": "book.14", "predicate": "on", "object": "shelf.12"}, {"subject": "man.2", "predicate": "wearing", "object": "pant.10"}, {"subject": "man.2", "predicate": "has", "object": "shirt.4"}, {"subject": "shelf.3", "predicate": "of", "object": "book.9"}]
9
Generate a structured scene graph for an image of size (640 x 480) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (640 x 480) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [2, 0, 637, 477]}, {"id": "window.2", "bbox": [452, 80, 550, 245]}, {"id": "desk.3", "bbox": [386, 365, 636, 478]}, {"id": "shelf.4", "bbox": [166, 136, 264, 361]}, {"id": "desk.5", "bbox": [259, 258, 437, 351]}, {"id": "desk.6", "bbox": [3, 385, 166, 478]}, {"id": "chair.7", "bbox": [151, 282, 253, 428]}, {"id": "window.8", "bbox": [352, 81, 445, 239]}, {"id": "window.9", "bbox": [562, 71, 638, 254]}, {"id": "desk.10", "bbox": [181, 313, 323, 447]}, {"id": "building.11", "bbox": [560, 125, 636, 253]}, {"id": "desk.12", "bbox": [501, 240, 636, 300]}, {"id": "desk.13", "bbox": [249, 245, 433, 288]}, {"id": "tower.14", "bbox": [251, 275, 308, 349]}, {"id": "light.15", "bbox": [207, 5, 538, 93]}, {"id": "book.16", "bbox": [408, 380, 488, 423]}, {"id": "light.17", "bbox": [215, 30, 290, 51]}, {"id": "paper.18", "bbox": [425, 365, 593, 464]}, {"id": "book.19", "bbox": [195, 214, 254, 257]}, {"id": "chair.20", "bbox": [493, 221, 522, 279]}, {"id": "light.21", "bbox": [470, 61, 533, 81]}, {"id": "light.22", "bbox": [320, 71, 383, 90]}, {"id": "bottle.23", "bbox": [124, 279, 148, 318]}]
[{"subject": "room.1", "predicate": "has", "object": "light.15"}, {"subject": "bottle.23", "predicate": "on", "object": "desk.10"}, {"subject": "light.15", "predicate": "in", "object": "room.1"}, {"subject": "light.17", "predicate": "in", "object": "room.1"}, {"subject": "bottle.23", "predicate": "on", "object": "desk.10"}, {"subject": "tower.14", "predicate": "under", "object": "desk.13"}, {"subject": "light.15", "predicate": "in", "object": "room.1"}, {"subject": "book.19", "predicate": "on", "object": "shelf.4"}, {"subject": "book.16", "predicate": "on", "object": "desk.3"}, {"subject": "light.15", "predicate": "in", "object": "room.1"}, {"subject": "book.16", "predicate": "with", "object": "paper.18"}, {"subject": "book.16", "predicate": "on", "object": "desk.3"}]
10
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 380, 471, 596]}, {"id": "paper.2", "bbox": [0, 417, 63, 436]}, {"id": "table.3", "bbox": [0, 396, 202, 596]}, {"id": "chair.4", "bbox": [556, 355, 712, 550]}, {"id": "coat.5", "bbox": [532, 355, 669, 567]}, {"id": "desk.6", "bbox": [697, 365, 799, 414]}, {"id": "man.7", "bbox": [580, 267, 699, 412]}, {"id": "board.8", "bbox": [140, 139, 245, 284]}, {"id": "shirt.9", "bbox": [584, 303, 698, 410]}, {"id": "book.10", "bbox": [550, 269, 588, 328]}, {"id": "screen.11", "bbox": [308, 291, 393, 366]}, {"id": "paper.12", "bbox": [455, 403, 500, 449]}, {"id": "paper.13", "bbox": [457, 357, 553, 396]}, {"id": "shelf.14", "bbox": [457, 328, 585, 344]}, {"id": "shelf.15", "bbox": [507, 475, 566, 504]}, {"id": "shelf.16", "bbox": [467, 387, 555, 400]}, {"id": "cup.17", "bbox": [57, 439, 89, 477]}, {"id": "paper.18", "bbox": [505, 451, 546, 482]}, {"id": "shelf.19", "bbox": [483, 437, 542, 456]}]
[{"subject": "man.7", "predicate": "has", "object": "coat.5"}, {"subject": "coat.5", "predicate": "on", "object": "chair.4"}, {"subject": "paper.13", "predicate": "under", "object": "shelf.14"}, {"subject": "paper.13", "predicate": "on", "object": "shelf.16"}, {"subject": "paper.12", "predicate": "under", "object": "shelf.16"}, {"subject": "paper.12", "predicate": "on", "object": "shelf.19"}, {"subject": "paper.18", "predicate": "under", "object": "shelf.19"}, {"subject": "paper.18", "predicate": "on", "object": "shelf.15"}, {"subject": "cup.17", "predicate": "on", "object": "desk.1"}, {"subject": "paper.2", "predicate": "on", "object": "desk.1"}, {"subject": "man.7", "predicate": "has", "object": "coat.5"}, {"subject": "shelf.14", "predicate": "has", "object": "book.10"}, {"subject": "coat.5", "predicate": "on", "object": "chair.4"}, {"subject": "coat.5", "predicate": "on", "object": "chair.4"}, {"subject": "man.7", "predicate": "wears", "object": "shirt.9"}, {"subject": "coat.5", "predicate": "on", "object": "chair.4"}, {"subject": "cup.17", "predicate": "on", "object": "desk.1"}, {"subject": "coat.5", "predicate": "on back of", "object": "chair.4"}, {"subject": "cup.17", "predicate": "on", "object": "desk.1"}, {"subject": "paper.2", "predicate": "sitting on", "object": "desk.1"}, {"subject": "paper.2", "predicate": "lying on", "object": "desk.1"}, {"subject": "cup.17", "predicate": "on", "object": "desk.1"}, {"subject": "man.7", "predicate": "wearing", "object": "shirt.9"}, {"subject": "book.10", "predicate": "on", "object": "shelf.14"}]
11
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "chair.1", "bbox": [248, 171, 539, 598]}, {"id": "desk.2", "bbox": [0, 165, 578, 256]}, {"id": "window.3", "bbox": [490, 0, 796, 181]}, {"id": "leg.4", "bbox": [253, 512, 359, 556]}, {"id": "cabinet.5", "bbox": [405, 250, 546, 444]}, {"id": "table.6", "bbox": [607, 179, 798, 270]}, {"id": "box.7", "bbox": [0, 247, 159, 421]}, {"id": "building.8", "bbox": [621, 0, 798, 181]}, {"id": "bag.9", "bbox": [30, 394, 111, 453]}, {"id": "bag.10", "bbox": [340, 153, 467, 217]}, {"id": "bowl.11", "bbox": [620, 153, 670, 192]}, {"id": "pot.12", "bbox": [776, 142, 796, 198]}, {"id": "plant.13", "bbox": [736, 107, 799, 153]}, {"id": "plate.14", "bbox": [611, 172, 667, 202]}, {"id": "flower.15", "bbox": [758, 110, 796, 142]}, {"id": "plant.16", "bbox": [769, 117, 796, 204]}]
[{"subject": "chair.1", "predicate": "at", "object": "desk.2"}, {"subject": "box.7", "predicate": "under", "object": "desk.2"}, {"subject": "box.7", "predicate": "under", "object": "desk.2"}, {"subject": "bowl.11", "predicate": "on", "object": "table.6"}, {"subject": "pot.12", "predicate": "has", "object": "flower.15"}, {"subject": "plate.14", "predicate": "on", "object": "desk.2"}, {"subject": "plant.16", "predicate": "on", "object": "table.6"}, {"subject": "box.7", "predicate": "under", "object": "desk.2"}, {"subject": "bowl.11", "predicate": "on", "object": "table.6"}, {"subject": "bag.10", "predicate": "on", "object": "desk.2"}, {"subject": "chair.1", "predicate": "near", "object": "table.6"}]
12
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 321, 799, 520]}, {"id": "cup.2", "bbox": [640, 305, 695, 367]}]
[{"subject": "cup.2", "predicate": "on", "object": "desk.1"}]
13
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 182, 590, 596]}, {"id": "chair.2", "bbox": [604, 266, 796, 597]}, {"id": "book.3", "bbox": [71, 417, 307, 541]}, {"id": "paper.4", "bbox": [0, 508, 251, 596]}, {"id": "cup.5", "bbox": [12, 318, 71, 382]}]
[{"subject": "book.3", "predicate": "on", "object": "desk.1"}, {"subject": "book.3", "predicate": "on", "object": "desk.1"}, {"subject": "book.3", "predicate": "on", "object": "desk.1"}, {"subject": "cup.5", "predicate": "on", "object": "desk.1"}, {"subject": "chair.2", "predicate": "in front of", "object": "desk.1"}]
14
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 329, 799, 596]}, {"id": "people.2", "bbox": [32, 87, 359, 321]}, {"id": "lamp.3", "bbox": [628, 168, 797, 435]}, {"id": "book.4", "bbox": [0, 527, 188, 597]}, {"id": "bottle.5", "bbox": [316, 478, 375, 574]}]
[{"subject": "book.4", "predicate": "on", "object": "desk.1"}, {"subject": "bottle.5", "predicate": "on", "object": "desk.1"}, {"subject": "book.4", "predicate": "on", "object": "desk.1"}, {"subject": "lamp.3", "predicate": "on", "object": "desk.1"}, {"subject": "people.2", "predicate": "on", "object": "desk.1"}]
15
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 798, 599]}, {"id": "desk.2", "bbox": [0, 277, 799, 597]}, {"id": "paper.3", "bbox": [574, 279, 795, 449]}, {"id": "box.4", "bbox": [0, 170, 55, 314]}, {"id": "box.5", "bbox": [0, 395, 62, 446]}]
[{"subject": "paper.3", "predicate": "on", "object": "desk.2"}, {"subject": "paper.3", "predicate": "on", "object": "desk.2"}, {"subject": "paper.3", "predicate": "above", "object": "desk.2"}, {"subject": "paper.3", "predicate": "on", "object": "desk.2"}, {"subject": "paper.3", "predicate": "on", "object": "desk.2"}]
16
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "sidewalk.1", "bbox": [106, 231, 796, 596]}, {"id": "street.2", "bbox": [0, 217, 284, 597]}, {"id": "street.3", "bbox": [0, 491, 799, 597]}, {"id": "guy.4", "bbox": [296, 196, 545, 379]}, {"id": "building.5", "bbox": [60, 0, 212, 152]}, {"id": "car.6", "bbox": [92, 221, 241, 353]}, {"id": "pole.7", "bbox": [199, 139, 221, 519]}, {"id": "man.8", "bbox": [448, 162, 518, 375]}, {"id": "boy.9", "bbox": [287, 210, 349, 374]}, {"id": "jacket.10", "bbox": [286, 231, 425, 306]}, {"id": "boy.11", "bbox": [365, 233, 425, 377]}, {"id": "jacket.12", "bbox": [364, 254, 425, 307]}, {"id": "tree.13", "bbox": [44, 128, 126, 230]}, {"id": "window.14", "bbox": [85, 25, 212, 63]}, {"id": "bag.15", "bbox": [500, 302, 527, 357]}, {"id": "jacket.16", "bbox": [400, 212, 455, 283]}, {"id": "jean.17", "bbox": [461, 285, 503, 363]}, {"id": "pant.18", "bbox": [298, 285, 335, 364]}, {"id": "fence.19", "bbox": [305, 200, 371, 220]}, {"id": "street.20", "bbox": [17, 200, 144, 481]}, {"id": "people.21", "bbox": [295, 170, 564, 389]}, {"id": "sign.22", "bbox": [188, 46, 247, 139]}]
[{"subject": "boy.9", "predicate": "with", "object": "pant.18"}, {"subject": "man.8", "predicate": "has", "object": "jean.17"}, {"subject": "man.8", "predicate": "walking on", "object": "street.3"}, {"subject": "boy.11", "predicate": "walking on", "object": "street.3"}, {"subject": "boy.9", "predicate": "walking on", "object": "street.3"}, {"subject": "car.6", "predicate": "on", "object": "street.20"}, {"subject": "people.21", "predicate": "on", "object": "sidewalk.1"}, {"subject": "boy.11", "predicate": "has", "object": "jacket.12"}, {"subject": "sign.22", "predicate": "on", "object": "pole.7"}, {"subject": "jacket.12", "predicate": "on", "object": "boy.11"}, {"subject": "sign.22", "predicate": "on", "object": "sidewalk.1"}, {"subject": "man.8", "predicate": "on", "object": "sidewalk.1"}, {"subject": "building.5", "predicate": "has", "object": "window.14"}, {"subject": "sign.22", "predicate": "on", "object": "pole.7"}, {"subject": "building.5", "predicate": "with", "object": "window.14"}]
17
Generate a structured scene graph for an image of size (800 x 640) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 640) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tile.1", "bbox": [0, 417, 798, 637]}, {"id": "chair.2", "bbox": [0, 321, 351, 637]}, {"id": "desk.3", "bbox": [130, 319, 510, 444]}, {"id": "chair.4", "bbox": [199, 325, 354, 560]}, {"id": "cabinet.5", "bbox": [628, 275, 728, 542]}, {"id": "wheel.6", "bbox": [203, 484, 350, 564]}, {"id": "cabinet.7", "bbox": [92, 394, 146, 446]}, {"id": "door.8", "bbox": [661, 291, 705, 530]}, {"id": "laptop.9", "bbox": [385, 250, 469, 330]}, {"id": "light.10", "bbox": [317, 253, 397, 310]}, {"id": "stand.11", "bbox": [434, 307, 468, 339]}, {"id": "chair.12", "bbox": [192, 328, 367, 564]}, {"id": "laptop.13", "bbox": [401, 240, 464, 338]}]
[{"subject": "laptop.9", "predicate": "on", "object": "desk.3"}, {"subject": "laptop.9", "predicate": "on", "object": "desk.3"}, {"subject": "cabinet.5", "predicate": "has", "object": "door.8"}, {"subject": "chair.4", "predicate": "has", "object": "wheel.6"}, {"subject": "laptop.9", "predicate": "on", "object": "stand.11"}, {"subject": "chair.4", "predicate": "has", "object": "wheel.6"}]
18
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "window.1", "bbox": [0, 0, 315, 385]}, {"id": "desk.2", "bbox": [0, 329, 570, 596]}, {"id": "shelf.3", "bbox": [447, 103, 646, 384]}, {"id": "man.4", "bbox": [622, 257, 797, 568]}, {"id": "chair.5", "bbox": [592, 386, 797, 596]}, {"id": "shirt.6", "bbox": [622, 344, 799, 542]}, {"id": "bag.7", "bbox": [0, 352, 70, 444]}, {"id": "hair.8", "bbox": [670, 257, 745, 331]}, {"id": "bottle.9", "bbox": [117, 358, 144, 435]}, {"id": "helmet.10", "bbox": [469, 138, 508, 175]}, {"id": "table.11", "bbox": [1, 326, 564, 468]}]
[{"subject": "man.4", "predicate": "has", "object": "hair.8"}, {"subject": "bag.7", "predicate": "on", "object": "table.11"}, {"subject": "helmet.10", "predicate": "on", "object": "shelf.3"}]
19
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 360, 798, 521]}, {"id": "desk.2", "bbox": [400, 396, 798, 517]}, {"id": "roof.3", "bbox": [0, 0, 799, 51]}, {"id": "chair.4", "bbox": [31, 515, 287, 596]}, {"id": "wire.5", "bbox": [247, 236, 657, 260]}]
[{"subject": "chair.4", "predicate": "in front of", "object": "desk.1"}]
20
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "man.1", "bbox": [89, 229, 283, 500]}, {"id": "chair.2", "bbox": [60, 382, 319, 596]}, {"id": "window.3", "bbox": [343, 0, 510, 316]}, {"id": "desk.4", "bbox": [39, 350, 505, 420]}, {"id": "shirt.5", "bbox": [90, 329, 282, 470]}, {"id": "box.6", "bbox": [587, 419, 708, 597]}, {"id": "book.7", "bbox": [665, 160, 796, 315]}, {"id": "shelf.8", "bbox": [606, 247, 796, 395]}, {"id": "book.9", "bbox": [615, 0, 793, 128]}, {"id": "hair.10", "bbox": [142, 232, 213, 309]}, {"id": "bottle.11", "bbox": [407, 322, 435, 389]}, {"id": "book.12", "bbox": [339, 347, 409, 378]}, {"id": "table.13", "bbox": [272, 384, 507, 421]}]
[{"subject": "book.7", "predicate": "on", "object": "shelf.8"}, {"subject": "desk.4", "predicate": "made of", "object": "book.9"}, {"subject": "book.7", "predicate": "on", "object": "shelf.8"}, {"subject": "man.1", "predicate": "wearing", "object": "shirt.5"}, {"subject": "bottle.11", "predicate": "above", "object": "table.13"}, {"subject": "book.12", "predicate": "above", "object": "table.13"}]
21
Generate a structured scene graph for an image of size (256 x 256) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (256 x 256) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "person.1", "bbox": [0, 0, 191, 246]}, {"id": "pillow.2", "bbox": [135, 0, 241, 202]}, {"id": "girl.3", "bbox": [99, 94, 174, 210]}, {"id": "hair.4", "bbox": [95, 0, 162, 74]}, {"id": "hair.5", "bbox": [127, 93, 173, 145]}, {"id": "arm.6", "bbox": [134, 85, 190, 217]}, {"id": "hand.7", "bbox": [108, 202, 143, 246]}, {"id": "neck.8", "bbox": [108, 63, 136, 85]}, {"id": "woman.9", "bbox": [86, 5, 176, 152]}, {"id": "people.10", "bbox": [52, 18, 196, 172]}, {"id": "shirt.11", "bbox": [76, 50, 188, 133]}]
[{"subject": "girl.3", "predicate": "near", "object": "woman.9"}, {"subject": "woman.9", "predicate": "with", "object": "girl.3"}, {"subject": "woman.9", "predicate": "with", "object": "girl.3"}, {"subject": "person.1", "predicate": "has", "object": "hair.4"}, {"subject": "person.1", "predicate": "wearing", "object": "shirt.11"}]
22
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 335, 799, 403]}, {"id": "building.2", "bbox": [454, 0, 642, 338]}, {"id": "train.3", "bbox": [61, 257, 409, 389]}, {"id": "building.4", "bbox": [671, 0, 798, 358]}, {"id": "pole.5", "bbox": [547, 0, 579, 463]}, {"id": "pole.6", "bbox": [96, 159, 107, 278]}, {"id": "car.7", "bbox": [575, 319, 642, 364]}, {"id": "door.8", "bbox": [283, 285, 300, 378]}, {"id": "door.9", "bbox": [205, 288, 225, 369]}, {"id": "car.10", "bbox": [496, 325, 550, 357]}, {"id": "car.11", "bbox": [407, 321, 499, 356]}, {"id": "car.12", "bbox": [437, 327, 500, 357]}, {"id": "door.13", "bbox": [169, 290, 188, 367]}, {"id": "car.14", "bbox": [400, 321, 444, 353]}]
[{"subject": "car.7", "predicate": "on", "object": "street.1"}, {"subject": "car.10", "predicate": "on", "object": "street.1"}, {"subject": "car.12", "predicate": "on", "object": "street.1"}, {"subject": "car.14", "predicate": "on", "object": "street.1"}, {"subject": "train.3", "predicate": "has", "object": "door.8"}, {"subject": "train.3", "predicate": "has", "object": "door.9"}, {"subject": "train.3", "predicate": "has", "object": "door.13"}]
23
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [318, 0, 796, 286]}, {"id": "car.2", "bbox": [19, 196, 585, 431]}, {"id": "building.3", "bbox": [0, 0, 402, 389]}, {"id": "tree.4", "bbox": [315, 0, 627, 202]}, {"id": "tree.5", "bbox": [592, 0, 796, 245]}, {"id": "car.6", "bbox": [552, 243, 797, 387]}, {"id": "girl.7", "bbox": [104, 200, 168, 441]}, {"id": "door.8", "bbox": [0, 161, 84, 307]}, {"id": "girl.9", "bbox": [96, 201, 246, 443]}, {"id": "person.10", "bbox": [0, 203, 49, 397]}, {"id": "tire.11", "bbox": [317, 350, 398, 439]}, {"id": "girl.12", "bbox": [165, 204, 220, 434]}, {"id": "sign.13", "bbox": [197, 0, 250, 119]}, {"id": "shirt.14", "bbox": [0, 225, 47, 306]}, {"id": "shirt.15", "bbox": [199, 252, 245, 317]}, {"id": "wheel.16", "bbox": [709, 325, 759, 385]}, {"id": "sign.17", "bbox": [351, 69, 401, 122]}, {"id": "man.18", "bbox": [607, 260, 662, 295]}, {"id": "sign.19", "bbox": [578, 105, 608, 158]}, {"id": "sign.20", "bbox": [119, 140, 158, 189]}, {"id": "shirt.21", "bbox": [178, 253, 209, 303]}]
[{"subject": "car.2", "predicate": "behind", "object": "girl.9"}, {"subject": "girl.9", "predicate": "near", "object": "car.2"}, {"subject": "girl.12", "predicate": "near", "object": "car.2"}, {"subject": "girl.7", "predicate": "near", "object": "car.2"}, {"subject": "man.18", "predicate": "in", "object": "car.6"}, {"subject": "car.2", "predicate": "has", "object": "tire.11"}]
24
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [228, 0, 796, 427]}, {"id": "street.2", "bbox": [111, 257, 798, 596]}, {"id": "building.3", "bbox": [0, 0, 142, 374]}, {"id": "car.4", "bbox": [453, 259, 677, 418]}, {"id": "bike.5", "bbox": [637, 330, 792, 480]}, {"id": "bike.6", "bbox": [322, 268, 492, 353]}, {"id": "pole.7", "bbox": [114, 275, 147, 437]}, {"id": "boy.8", "bbox": [36, 282, 73, 344]}, {"id": "pole.9", "bbox": [451, 142, 467, 284]}, {"id": "man.10", "bbox": [418, 225, 452, 291]}, {"id": "motorcycle.11", "bbox": [187, 254, 239, 285]}, {"id": "street.12", "bbox": [164, 305, 516, 596]}, {"id": "man.13", "bbox": [34, 278, 78, 349]}]
[{"subject": "tree.1", "predicate": "on", "object": "street.12"}, {"subject": "man.10", "predicate": "near", "object": "bike.6"}, {"subject": "tree.1", "predicate": "along", "object": "street.12"}, {"subject": "tree.1", "predicate": "along", "object": "street.12"}, {"subject": "bike.5", "predicate": "on", "object": "street.12"}, {"subject": "building.3", "predicate": "on", "object": "street.2"}]
25
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 640, 418]}, {"id": "sidewalk.2", "bbox": [0, 350, 799, 596]}, {"id": "people.3", "bbox": [88, 278, 319, 455]}, {"id": "pole.4", "bbox": [682, 17, 742, 351]}, {"id": "table.5", "bbox": [471, 330, 721, 405]}, {"id": "plant.6", "bbox": [232, 119, 360, 258]}, {"id": "car.7", "bbox": [638, 288, 786, 369]}, {"id": "umbrella.8", "bbox": [403, 237, 733, 279]}, {"id": "woman.9", "bbox": [185, 280, 245, 454]}, {"id": "woman.10", "bbox": [269, 289, 319, 450]}, {"id": "man.11", "bbox": [136, 278, 184, 430]}, {"id": "door.12", "bbox": [373, 271, 423, 353]}, {"id": "person.13", "bbox": [604, 317, 653, 387]}, {"id": "chair.14", "bbox": [540, 349, 592, 407]}, {"id": "window.15", "bbox": [411, 93, 453, 150]}, {"id": "chair.16", "bbox": [708, 355, 756, 407]}, {"id": "car.17", "bbox": [639, 285, 678, 341]}, {"id": "chair.18", "bbox": [492, 357, 530, 406]}, {"id": "chair.19", "bbox": [653, 360, 688, 406]}, {"id": "chair.20", "bbox": [605, 347, 644, 391]}, {"id": "chair.21", "bbox": [447, 350, 485, 392]}, {"id": "car.22", "bbox": [734, 302, 798, 367]}]
[{"subject": "person.13", "predicate": "on", "object": "chair.20"}, {"subject": "car.22", "predicate": "near", "object": "sidewalk.2"}, {"subject": "car.7", "predicate": "near", "object": "sidewalk.2"}, {"subject": "car.17", "predicate": "near", "object": "sidewalk.2"}]
26
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [62, 396, 796, 598]}, {"id": "building.2", "bbox": [0, 0, 51, 367]}, {"id": "people.3", "bbox": [339, 360, 719, 523]}, {"id": "building.4", "bbox": [506, 100, 670, 377]}, {"id": "building.5", "bbox": [121, 17, 208, 346]}, {"id": "building.6", "bbox": [37, 140, 176, 318]}, {"id": "building.7", "bbox": [649, 93, 795, 300]}, {"id": "tree.8", "bbox": [0, 232, 82, 390]}, {"id": "building.9", "bbox": [414, 135, 507, 342]}, {"id": "flower.10", "bbox": [318, 401, 559, 453]}, {"id": "sign.11", "bbox": [671, 93, 752, 285]}, {"id": "men.12", "bbox": [558, 360, 720, 521]}, {"id": "car.13", "bbox": [75, 396, 185, 478]}, {"id": "building.14", "bbox": [228, 217, 284, 358]}, {"id": "bus.15", "bbox": [561, 356, 643, 401]}, {"id": "man.16", "bbox": [617, 386, 664, 511]}, {"id": "man.17", "bbox": [389, 377, 428, 514]}, {"id": "vehicle.18", "bbox": [541, 387, 605, 410]}, {"id": "tree.19", "bbox": [67, 283, 146, 393]}, {"id": "car.20", "bbox": [0, 403, 87, 471]}, {"id": "tree.21", "bbox": [125, 313, 206, 389]}, {"id": "paper.22", "bbox": [605, 433, 668, 467]}, {"id": "building.23", "bbox": [407, 137, 498, 231]}]
[{"subject": "man.17", "predicate": "walking on", "object": "street.1"}, {"subject": "sign.11", "predicate": "on", "object": "building.7"}]
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Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 794, 596]}, {"id": "table.2", "bbox": [182, 294, 623, 596]}, {"id": "chair.3", "bbox": [271, 288, 621, 592]}, {"id": "door.4", "bbox": [2, 0, 139, 599]}, {"id": "tile.5", "bbox": [150, 0, 495, 30]}]
[{"subject": "chair.3", "predicate": "in", "object": "room.1"}, {"subject": "chair.3", "predicate": "in", "object": "room.1"}, {"subject": "chair.3", "predicate": "in", "object": "room.1"}, {"subject": "chair.3", "predicate": "in", "object": "room.1"}, {"subject": "chair.3", "predicate": "in", "object": "room.1"}, {"subject": "door.4", "predicate": "in", "object": "room.1"}, {"subject": "chair.3", "predicate": "near", "object": "table.2"}]
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Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 800, 597]}, {"id": "table.2", "bbox": [80, 453, 533, 598]}, {"id": "pillow.3", "bbox": [325, 468, 796, 595]}, {"id": "flower.4", "bbox": [199, 337, 412, 482]}, {"id": "curtain.5", "bbox": [773, 0, 798, 574]}, {"id": "stand.6", "bbox": [515, 371, 603, 535]}, {"id": "pillow.7", "bbox": [352, 367, 480, 475]}, {"id": "pillow.8", "bbox": [20, 393, 118, 493]}, {"id": "bowl.9", "bbox": [121, 429, 254, 500]}, {"id": "basket.10", "bbox": [556, 457, 646, 527]}, {"id": "vase.11", "bbox": [268, 418, 339, 487]}, {"id": "book.12", "bbox": [343, 457, 470, 489]}, {"id": "basket.13", "bbox": [503, 315, 570, 360]}]
[{"subject": "bowl.9", "predicate": "on", "object": "table.2"}, {"subject": "vase.11", "predicate": "has", "object": "flower.4"}, {"subject": "vase.11", "predicate": "on", "object": "table.2"}, {"subject": "bowl.9", "predicate": "on", "object": "table.2"}, {"subject": "vase.11", "predicate": "of", "object": "flower.4"}, {"subject": "book.12", "predicate": "on", "object": "table.2"}, {"subject": "book.12", "predicate": "on", "object": "table.2"}, {"subject": "flower.4", "predicate": "on", "object": "table.2"}, {"subject": "flower.4", "predicate": "on", "object": "table.2"}, {"subject": "flower.4", "predicate": "on", "object": "table.2"}, {"subject": "book.12", "predicate": "on", "object": "table.2"}, {"subject": "pillow.3", "predicate": "in", "object": "room.1"}, {"subject": "bowl.9", "predicate": "on", "object": "table.2"}, {"subject": "book.12", "predicate": "on", "object": "table.2"}, {"subject": "flower.4", "predicate": "on", "object": "table.2"}, {"subject": "bowl.9", "predicate": "on", "object": "table.2"}, {"subject": "vase.11", "predicate": "of", "object": "flower.4"}, {"subject": "book.12", "predicate": "on", "object": "table.2"}]
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Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "beach.1", "bbox": [0, 247, 800, 596]}, {"id": "woman.2", "bbox": [308, 160, 725, 414]}, {"id": "building.3", "bbox": [0, 185, 293, 289]}, {"id": "boat.4", "bbox": [464, 235, 674, 383]}, {"id": "pole.5", "bbox": [84, 0, 154, 314]}, {"id": "railing.6", "bbox": [0, 196, 295, 290]}, {"id": "house.7", "bbox": [0, 192, 78, 275]}, {"id": "short.8", "bbox": [318, 269, 368, 325]}, {"id": "hair.9", "bbox": [324, 160, 356, 194]}]
[{"subject": "building.3", "predicate": "on", "object": "beach.1"}, {"subject": "boat.4", "predicate": "on", "object": "beach.1"}, {"subject": "woman.2", "predicate": "wearing", "object": "short.8"}, {"subject": "pole.5", "predicate": "above", "object": "beach.1"}, {"subject": "house.7", "predicate": "above", "object": "beach.1"}, {"subject": "boat.4", "predicate": "above", "object": "beach.1"}, {"subject": "woman.2", "predicate": "above", "object": "beach.1"}]
30
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "stand.1", "bbox": [465, 112, 796, 594]}, {"id": "curtain.2", "bbox": [151, 40, 453, 450]}, {"id": "window.3", "bbox": [372, 104, 397, 316]}, {"id": "window.4", "bbox": [0, 105, 103, 478]}, {"id": "chair.5", "bbox": [203, 307, 310, 445]}, {"id": "drawer.6", "bbox": [496, 376, 754, 468]}, {"id": "table.7", "bbox": [256, 307, 404, 451]}, {"id": "chair.8", "bbox": [292, 297, 382, 431]}, {"id": "bag.9", "bbox": [107, 410, 189, 478]}, {"id": "light.10", "bbox": [335, 52, 389, 82]}]
[{"subject": "table.7", "predicate": "made of", "object": "chair.5"}, {"subject": "curtain.2", "predicate": "near", "object": "window.3"}, {"subject": "table.7", "predicate": "with", "object": "chair.5"}]
31
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "cabinet.1", "bbox": [0, 0, 743, 287]}, {"id": "sink.2", "bbox": [213, 417, 483, 485]}, {"id": "shelf.3", "bbox": [204, 244, 481, 274]}, {"id": "plant.4", "bbox": [82, 351, 160, 426]}, {"id": "flower.5", "bbox": [86, 351, 156, 399]}, {"id": "glass.6", "bbox": [571, 378, 602, 426]}, {"id": "box.7", "bbox": [210, 216, 259, 247]}]
[{"subject": "plant.4", "predicate": "has", "object": "flower.5"}]
32
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "cabinet.1", "bbox": [0, 0, 754, 323]}, {"id": "counter.2", "bbox": [36, 365, 756, 521]}, {"id": "cabinet.3", "bbox": [42, 435, 749, 598]}, {"id": "door.4", "bbox": [271, 0, 420, 311]}, {"id": "cabinet.5", "bbox": [413, 0, 505, 316]}, {"id": "glass.6", "bbox": [349, 17, 404, 291]}, {"id": "sink.7", "bbox": [263, 417, 422, 455]}, {"id": "cup.8", "bbox": [365, 192, 404, 226]}, {"id": "tile.9", "bbox": [56, 303, 120, 350]}, {"id": "handle.10", "bbox": [409, 357, 446, 395]}, {"id": "shelf.11", "bbox": [251, 0, 421, 315]}]
[{"subject": "cabinet.1", "predicate": "has", "object": "glass.6"}, {"subject": "shelf.11", "predicate": "has", "object": "cup.8"}]
33
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 794, 598]}, {"id": "seat.2", "bbox": [0, 278, 523, 442]}, {"id": "table.3", "bbox": [10, 376, 362, 567]}, {"id": "window.4", "bbox": [85, 19, 290, 278]}, {"id": "tile.5", "bbox": [575, 493, 699, 582]}, {"id": "tile.6", "bbox": [682, 510, 796, 584]}, {"id": "pillow.7", "bbox": [60, 285, 156, 360]}, {"id": "pillow.8", "bbox": [252, 289, 317, 347]}, {"id": "pillow.9", "bbox": [321, 280, 408, 350]}, {"id": "door.10", "bbox": [591, 180, 630, 294]}, {"id": "pillow.11", "bbox": [188, 275, 266, 342]}, {"id": "tile.12", "bbox": [585, 479, 684, 525]}, {"id": "pillow.13", "bbox": [407, 285, 474, 341]}, {"id": "tile.14", "bbox": [476, 472, 567, 514]}, {"id": "bottle.15", "bbox": [220, 376, 270, 428]}, {"id": "stand.16", "bbox": [725, 361, 791, 425]}, {"id": "glass.17", "bbox": [240, 383, 270, 427]}, {"id": "glass.18", "bbox": [192, 379, 269, 403]}]
[{"subject": "pillow.7", "predicate": "above", "object": "seat.2"}, {"subject": "pillow.11", "predicate": "above", "object": "seat.2"}, {"subject": "pillow.8", "predicate": "above", "object": "seat.2"}, {"subject": "pillow.9", "predicate": "above", "object": "seat.2"}, {"subject": "pillow.13", "predicate": "above", "object": "seat.2"}, {"subject": "room.1", "predicate": "in", "object": "window.4"}, {"subject": "room.1", "predicate": "has", "object": "window.4"}]
35
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "desk.1", "bbox": [0, 0, 798, 596]}, {"id": "chair.2", "bbox": [168, 230, 771, 580]}, {"id": "table.3", "bbox": [207, 266, 724, 580]}, {"id": "wheel.4", "bbox": [166, 392, 289, 428]}, {"id": "desk.5", "bbox": [40, 0, 289, 435]}, {"id": "book.6", "bbox": [32, 26, 798, 140]}, {"id": "lamp.7", "bbox": [478, 127, 685, 277]}, {"id": "flower.8", "bbox": [503, 0, 639, 174]}, {"id": "drawer.9", "bbox": [47, 267, 149, 415]}, {"id": "lamp.10", "bbox": [476, 179, 533, 232]}, {"id": "wheel.11", "bbox": [60, 402, 141, 433]}, {"id": "stand.12", "bbox": [527, 125, 696, 267]}]
[{"subject": "chair.2", "predicate": "in", "object": "desk.1"}, {"subject": "lamp.7", "predicate": "in", "object": "desk.1"}, {"subject": "lamp.10", "predicate": "on", "object": "stand.12"}, {"subject": "book.6", "predicate": "on", "object": "desk.5"}, {"subject": "chair.2", "predicate": "has", "object": "wheel.4"}, {"subject": "lamp.7", "predicate": "on", "object": "desk.1"}, {"subject": "book.6", "predicate": "on", "object": "desk.5"}, {"subject": "drawer.9", "predicate": "near", "object": "chair.2"}, {"subject": "book.6", "predicate": "on", "object": "desk.5"}]
36
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [28, 319, 633, 596]}, {"id": "cabinet.2", "bbox": [129, 64, 299, 344]}, {"id": "light.3", "bbox": [292, 97, 387, 274]}, {"id": "drawer.4", "bbox": [561, 318, 748, 364]}, {"id": "sink.5", "bbox": [133, 286, 232, 375]}, {"id": "fruit.6", "bbox": [192, 329, 316, 393]}, {"id": "drawer.7", "bbox": [0, 254, 130, 293]}, {"id": "bowl.8", "bbox": [200, 370, 309, 398]}, {"id": "drawer.9", "bbox": [400, 296, 488, 337]}, {"id": "pot.10", "bbox": [23, 135, 73, 167]}, {"id": "plate.11", "bbox": [704, 278, 742, 327]}, {"id": "pot.12", "bbox": [536, 275, 575, 308]}, {"id": "flower.13", "bbox": [343, 229, 391, 256]}, {"id": "window.14", "bbox": [292, 83, 385, 249]}, {"id": "food.15", "bbox": [167, 329, 463, 407]}]
[{"subject": "light.3", "predicate": "from", "object": "window.14"}, {"subject": "food.15", "predicate": "near", "object": "sink.5"}, {"subject": "bowl.8", "predicate": "on", "object": "table.1"}, {"subject": "bowl.8", "predicate": "of", "object": "fruit.6"}, {"subject": "bowl.8", "predicate": "of", "object": "fruit.6"}, {"subject": "plate.11", "predicate": "on", "object": "drawer.7"}, {"subject": "sink.5", "predicate": "on", "object": "table.1"}]
37
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 799, 597]}, {"id": "chair.2", "bbox": [0, 178, 278, 597]}, {"id": "pillow.3", "bbox": [226, 214, 750, 375]}, {"id": "plate.4", "bbox": [224, 0, 609, 211]}, {"id": "table.5", "bbox": [360, 371, 797, 570]}, {"id": "door.6", "bbox": [0, 0, 95, 296]}, {"id": "plant.7", "bbox": [650, 0, 796, 159]}, {"id": "pillow.8", "bbox": [22, 264, 168, 400]}, {"id": "bowl.9", "bbox": [663, 293, 797, 406]}, {"id": "book.10", "bbox": [425, 373, 564, 426]}, {"id": "flower.11", "bbox": [0, 259, 65, 415]}, {"id": "child.12", "bbox": [568, 250, 628, 317]}, {"id": "glass.13", "bbox": [361, 371, 796, 442]}, {"id": "leaf.14", "bbox": [0, 269, 61, 371]}, {"id": "plate.15", "bbox": [592, 397, 672, 414]}, {"id": "box.16", "bbox": [27, 382, 77, 415]}, {"id": "leg.17", "bbox": [236, 522, 261, 571]}, {"id": "glass.18", "bbox": [0, 393, 112, 430]}, {"id": "table.19", "bbox": [5, 396, 99, 496]}, {"id": "fruit.20", "bbox": [700, 292, 787, 312]}, {"id": "seat.21", "bbox": [3, 190, 260, 581]}, {"id": "basket.22", "bbox": [662, 294, 796, 368]}, {"id": "vase.23", "bbox": [0, 320, 27, 414]}, {"id": "bowl.24", "bbox": [660, 295, 796, 362]}, {"id": "leaf.25", "bbox": [658, 0, 795, 137]}]
[{"subject": "fruit.20", "predicate": "in", "object": "bowl.9"}, {"subject": "book.10", "predicate": "on", "object": "table.5"}, {"subject": "plate.15", "predicate": "on", "object": "table.5"}, {"subject": "fruit.20", "predicate": "on", "object": "table.5"}, {"subject": "plate.15", "predicate": "on", "object": "table.5"}, {"subject": "book.10", "predicate": "on", "object": "table.5"}, {"subject": "table.19", "predicate": "has", "object": "glass.18"}, {"subject": "table.5", "predicate": "has", "object": "glass.13"}, {"subject": "flower.11", "predicate": "near", "object": "seat.21"}, {"subject": "box.16", "predicate": "on", "object": "table.19"}, {"subject": "fruit.20", "predicate": "in", "object": "basket.22"}, {"subject": "book.10", "predicate": "sitting on", "object": "table.5"}, {"subject": "pillow.8", "predicate": "sitting on", "object": "chair.2"}, {"subject": "book.10", "predicate": "on", "object": "table.5"}, {"subject": "book.10", "predicate": "on", "object": "table.5"}, {"subject": "fruit.20", "predicate": "in", "object": "bowl.9"}, {"subject": "plate.15", "predicate": "on", "object": "table.5"}, {"subject": "vase.23", "predicate": "sitting on", "object": "table.19"}, {"subject": "leaf.25", "predicate": "on", "object": "plant.7"}, {"subject": "book.10", "predicate": "sitting on", "object": "table.5"}, {"subject": "plate.15", "predicate": "on", "object": "table.5"}]
38
Generate a structured scene graph for an image of size (782 x 800) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (782 x 800) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "basket.1", "bbox": [0, 669, 67, 751]}, {"id": "plant.2", "bbox": [215, 579, 552, 796]}, {"id": "pot.3", "bbox": [476, 565, 525, 604]}, {"id": "flower.4", "bbox": [0, 1, 677, 172]}, {"id": "flower.5", "bbox": [0, 516, 778, 758]}, {"id": "tree.6", "bbox": [0, 601, 93, 692]}, {"id": "table.7", "bbox": [144, 355, 386, 667]}, {"id": "chair.8", "bbox": [408, 378, 643, 622]}]
[{"subject": "table.7", "predicate": "has", "object": "plant.2"}, {"subject": "pot.3", "predicate": "has", "object": "flower.5"}, {"subject": "basket.1", "predicate": "with", "object": "flower.5"}, {"subject": "plant.2", "predicate": "has", "object": "table.7"}, {"subject": "chair.8", "predicate": "near", "object": "table.7"}, {"subject": "chair.8", "predicate": "near", "object": "table.7"}, {"subject": "plant.2", "predicate": "above", "object": "table.7"}]
39
Generate a structured scene graph for an image of size (800 x 534) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 534) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "plant.1", "bbox": [0, 253, 239, 531]}, {"id": "table.2", "bbox": [472, 246, 696, 437]}, {"id": "chair.3", "bbox": [371, 253, 542, 471]}, {"id": "chair.4", "bbox": [607, 240, 752, 438]}, {"id": "chair.5", "bbox": [421, 225, 564, 432]}, {"id": "chair.6", "bbox": [565, 225, 709, 419]}, {"id": "lamp.7", "bbox": [23, 117, 98, 241]}, {"id": "flower.8", "bbox": [558, 198, 590, 253]}]
[{"subject": "chair.3", "predicate": "at", "object": "table.2"}, {"subject": "chair.5", "predicate": "at", "object": "table.2"}, {"subject": "chair.6", "predicate": "at", "object": "table.2"}, {"subject": "chair.4", "predicate": "at", "object": "table.2"}, {"subject": "lamp.7", "predicate": "on", "object": "table.2"}, {"subject": "flower.8", "predicate": "on", "object": "table.2"}]
40
Generate a structured scene graph for an image of size (560 x 800) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (560 x 800) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [125, 335, 556, 745]}, {"id": "shelf.2", "bbox": [0, 0, 353, 446]}, {"id": "flower.3", "bbox": [162, 0, 557, 345]}, {"id": "chair.4", "bbox": [0, 292, 213, 763]}, {"id": "window.5", "bbox": [428, 0, 555, 530]}, {"id": "vase.6", "bbox": [372, 228, 460, 342]}, {"id": "tile.7", "bbox": [275, 755, 495, 797]}, {"id": "person.8", "bbox": [367, 296, 398, 342]}, {"id": "table.9", "bbox": [179, 683, 556, 796]}, {"id": "lamp.10", "bbox": [167, 101, 381, 346]}]
[{"subject": "table.1", "predicate": "near", "object": "chair.4"}, {"subject": "chair.4", "predicate": "near", "object": "table.1"}, {"subject": "flower.3", "predicate": "on", "object": "table.1"}, {"subject": "flower.3", "predicate": "in", "object": "vase.6"}, {"subject": "flower.3", "predicate": "in", "object": "vase.6"}, {"subject": "vase.6", "predicate": "on", "object": "table.1"}, {"subject": "lamp.10", "predicate": "on", "object": "table.1"}, {"subject": "flower.3", "predicate": "in", "object": "vase.6"}, {"subject": "chair.4", "predicate": "near", "object": "table.1"}, {"subject": "lamp.10", "predicate": "on", "object": "table.1"}, {"subject": "vase.6", "predicate": "on", "object": "table.1"}, {"subject": "lamp.10", "predicate": "on", "object": "table.1"}, {"subject": "lamp.10", "predicate": "on", "object": "table.1"}, {"subject": "chair.4", "predicate": "near", "object": "table.1"}, {"subject": "flower.3", "predicate": "in", "object": "vase.6"}, {"subject": "flower.3", "predicate": "on", "object": "table.1"}, {"subject": "table.1", "predicate": "near", "object": "chair.4"}]
41
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tile.1", "bbox": [0, 167, 727, 404]}, {"id": "tile.2", "bbox": [0, 367, 797, 594]}, {"id": "cabinet.3", "bbox": [0, 0, 284, 237]}, {"id": "stand.4", "bbox": [396, 0, 595, 240]}, {"id": "drawer.5", "bbox": [257, 489, 467, 599]}, {"id": "cup.6", "bbox": [540, 170, 606, 254]}, {"id": "bowl.7", "bbox": [463, 142, 536, 190]}, {"id": "handle.8", "bbox": [325, 530, 383, 553]}, {"id": "handle.9", "bbox": [0, 46, 23, 133]}, {"id": "box.10", "bbox": [461, 112, 527, 142]}, {"id": "handle.11", "bbox": [112, 52, 131, 125]}]
[{"subject": "cabinet.3", "predicate": "has", "object": "handle.11"}, {"subject": "cabinet.3", "predicate": "has", "object": "handle.9"}, {"subject": "bowl.7", "predicate": "on", "object": "stand.4"}]
42
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 795, 598]}, {"id": "chair.2", "bbox": [50, 402, 302, 594]}, {"id": "table.3", "bbox": [0, 379, 150, 593]}, {"id": "window.4", "bbox": [0, 0, 160, 185]}, {"id": "chair.5", "bbox": [396, 196, 482, 309]}, {"id": "book.6", "bbox": [32, 376, 117, 410]}, {"id": "building.7", "bbox": [21, 71, 48, 128]}, {"id": "chair.8", "bbox": [36, 382, 131, 435]}, {"id": "lamp.9", "bbox": [0, 3, 159, 185]}, {"id": "table.10", "bbox": [217, 289, 360, 442]}, {"id": "chair.11", "bbox": [226, 200, 311, 306]}]
[{"subject": "chair.8", "predicate": "in", "object": "room.1"}, {"subject": "lamp.9", "predicate": "in", "object": "room.1"}, {"subject": "chair.2", "predicate": "in", "object": "room.1"}, {"subject": "lamp.9", "predicate": "above", "object": "table.3"}, {"subject": "building.7", "predicate": "in", "object": "window.4"}, {"subject": "lamp.9", "predicate": "above", "object": "table.3"}, {"subject": "book.6", "predicate": "above", "object": "table.3"}, {"subject": "chair.2", "predicate": "in", "object": "room.1"}, {"subject": "chair.2", "predicate": "in", "object": "room.1"}, {"subject": "window.4", "predicate": "in", "object": "room.1"}, {"subject": "book.6", "predicate": "above", "object": "table.3"}, {"subject": "chair.11", "predicate": "near", "object": "window.4"}]
43
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tile.1", "bbox": [0, 40, 799, 379]}, {"id": "table.2", "bbox": [0, 335, 799, 596]}, {"id": "stand.3", "bbox": [690, 126, 796, 531]}, {"id": "cabinet.4", "bbox": [0, 0, 799, 65]}, {"id": "cabinet.5", "bbox": [0, 490, 288, 596]}, {"id": "plate.6", "bbox": [525, 525, 709, 588]}, {"id": "people.7", "bbox": [292, 247, 409, 359]}, {"id": "plate.8", "bbox": [0, 351, 119, 407]}, {"id": "wire.9", "bbox": [690, 313, 739, 357]}]
[{"subject": "plate.6", "predicate": "on", "object": "table.2"}, {"subject": "plate.8", "predicate": "on", "object": "table.2"}, {"subject": "plate.6", "predicate": "on", "object": "table.2"}, {"subject": "cabinet.5", "predicate": "under", "object": "table.2"}, {"subject": "plate.8", "predicate": "on", "object": "table.2"}, {"subject": "cabinet.5", "predicate": "under", "object": "table.2"}, {"subject": "stand.3", "predicate": "on", "object": "table.2"}, {"subject": "plate.6", "predicate": "on", "object": "table.2"}, {"subject": "plate.8", "predicate": "on", "object": "table.2"}]
44
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 2, 794, 592]}, {"id": "chair.2", "bbox": [150, 436, 437, 596]}, {"id": "desk.3", "bbox": [435, 417, 796, 595]}, {"id": "seat.4", "bbox": [457, 321, 653, 515]}, {"id": "chair.5", "bbox": [0, 32, 409, 220]}, {"id": "table.6", "bbox": [97, 214, 797, 520]}, {"id": "arm.7", "bbox": [164, 442, 425, 561]}, {"id": "stand.8", "bbox": [275, 84, 488, 282]}, {"id": "board.9", "bbox": [279, 85, 481, 223]}, {"id": "table.10", "bbox": [128, 243, 461, 307]}, {"id": "table.11", "bbox": [94, 42, 269, 194]}, {"id": "leg.12", "bbox": [456, 418, 587, 520]}, {"id": "leg.13", "bbox": [172, 530, 428, 596]}, {"id": "chair.14", "bbox": [339, 259, 456, 409]}, {"id": "table.15", "bbox": [443, 235, 678, 272]}, {"id": "chair.16", "bbox": [252, 217, 316, 297]}, {"id": "light.17", "bbox": [0, 8, 442, 47]}, {"id": "light.18", "bbox": [240, 18, 339, 31]}, {"id": "light.19", "bbox": [0, 9, 96, 23]}, {"id": "light.20", "bbox": [349, 21, 435, 34]}, {"id": "table.21", "bbox": [457, 314, 645, 528]}, {"id": "stand.22", "bbox": [150, 436, 437, 596]}, {"id": "chair.23", "bbox": [305, 221, 557, 296]}, {"id": "leg.24", "bbox": [207, 313, 270, 447]}, {"id": "chair.25", "bbox": [207, 278, 364, 449]}]
[{"subject": "table.21", "predicate": "has", "object": "leg.12"}, {"subject": "board.9", "predicate": "has", "object": "stand.8"}, {"subject": "stand.22", "predicate": "has", "object": "arm.7"}, {"subject": "stand.22", "predicate": "has", "object": "table.6"}, {"subject": "stand.22", "predicate": "has", "object": "board.9"}, {"subject": "chair.5", "predicate": "has", "object": "chair.16"}, {"subject": "table.6", "predicate": "in", "object": "room.1"}, {"subject": "chair.2", "predicate": "in", "object": "room.1"}, {"subject": "table.11", "predicate": "in", "object": "room.1"}, {"subject": "light.17", "predicate": "on", "object": "table.11"}, {"subject": "leg.24", "predicate": "on", "object": "chair.25"}, {"subject": "chair.2", "predicate": "in", "object": "room.1"}]
45
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 284, 305]}, {"id": "building.2", "bbox": [500, 0, 798, 307]}, {"id": "street.3", "bbox": [311, 245, 582, 329]}, {"id": "sidewalk.4", "bbox": [0, 273, 311, 332]}, {"id": "man.5", "bbox": [0, 229, 27, 318]}, {"id": "car.6", "bbox": [418, 258, 471, 293]}, {"id": "car.7", "bbox": [377, 252, 411, 283]}]
[{"subject": "man.5", "predicate": "on", "object": "sidewalk.4"}, {"subject": "man.5", "predicate": "walking on", "object": "sidewalk.4"}]
46
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [1, 3, 749, 447]}, {"id": "street.2", "bbox": [2, 428, 794, 597]}, {"id": "building.3", "bbox": [3, 0, 796, 403]}, {"id": "building.4", "bbox": [714, 2, 774, 355]}, {"id": "car.5", "bbox": [96, 371, 340, 475]}, {"id": "logo.6", "bbox": [366, 240, 535, 285]}, {"id": "vehicle.7", "bbox": [526, 342, 796, 477]}, {"id": "street.8", "bbox": [143, 457, 584, 590]}, {"id": "window.9", "bbox": [357, 128, 701, 247]}, {"id": "tire.10", "bbox": [578, 421, 636, 485]}, {"id": "window.11", "bbox": [353, 276, 703, 397]}, {"id": "window.12", "bbox": [0, 0, 693, 153]}, {"id": "light.13", "bbox": [154, 133, 675, 215]}]
[{"subject": "window.11", "predicate": "near", "object": "vehicle.7"}, {"subject": "car.5", "predicate": "on", "object": "street.2"}, {"subject": "logo.6", "predicate": "on", "object": "building.1"}, {"subject": "window.12", "predicate": "in", "object": "building.3"}, {"subject": "window.12", "predicate": "on", "object": "building.1"}]
47
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 630, 325]}, {"id": "street.2", "bbox": [0, 306, 799, 596]}, {"id": "sidewalk.3", "bbox": [0, 391, 799, 596]}, {"id": "tree.4", "bbox": [621, 136, 796, 328]}, {"id": "car.5", "bbox": [7, 286, 534, 343]}, {"id": "car.6", "bbox": [623, 298, 750, 337]}, {"id": "sign.7", "bbox": [44, 221, 167, 256]}, {"id": "bus.8", "bbox": [0, 227, 68, 341]}, {"id": "man.9", "bbox": [563, 290, 611, 417]}, {"id": "clock.10", "bbox": [649, 228, 676, 264]}, {"id": "flag.11", "bbox": [160, 196, 240, 245]}, {"id": "bus.12", "bbox": [372, 272, 446, 317]}, {"id": "umbrella.13", "bbox": [551, 283, 617, 311]}, {"id": "person.14", "bbox": [553, 275, 624, 423]}]
[{"subject": "person.14", "predicate": "with", "object": "umbrella.13"}, {"subject": "sign.7", "predicate": "on", "object": "building.1"}, {"subject": "flag.11", "predicate": "on", "object": "building.1"}, {"subject": "man.9", "predicate": "with", "object": "umbrella.13"}, {"subject": "building.1", "predicate": "on", "object": "street.2"}, {"subject": "man.9", "predicate": "carrying", "object": "umbrella.13"}, {"subject": "person.14", "predicate": "on", "object": "street.2"}, {"subject": "man.9", "predicate": "on", "object": "street.2"}, {"subject": "man.9", "predicate": "with", "object": "umbrella.13"}, {"subject": "man.9", "predicate": "carrying", "object": "umbrella.13"}, {"subject": "man.9", "predicate": "carrying", "object": "umbrella.13"}]
48
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 436, 394]}, {"id": "street.2", "bbox": [0, 353, 548, 555]}, {"id": "tree.3", "bbox": [7, 0, 370, 449]}, {"id": "leaf.4", "bbox": [3, 158, 151, 264]}, {"id": "pot.5", "bbox": [32, 407, 314, 589]}, {"id": "tree.6", "bbox": [394, 139, 617, 419]}, {"id": "building.7", "bbox": [425, 85, 572, 385]}, {"id": "people.8", "bbox": [522, 365, 579, 489]}, {"id": "window.9", "bbox": [225, 32, 431, 245]}, {"id": "leaf.10", "bbox": [401, 143, 621, 346]}, {"id": "pole.11", "bbox": [310, 201, 358, 503]}, {"id": "light.12", "bbox": [543, 114, 580, 380]}, {"id": "post.13", "bbox": [318, 200, 353, 477]}, {"id": "box.14", "bbox": [686, 354, 775, 475]}, {"id": "logo.15", "bbox": [34, 335, 65, 370]}, {"id": "man.16", "bbox": [716, 378, 782, 489]}, {"id": "plant.17", "bbox": [109, 398, 289, 462]}, {"id": "man.18", "bbox": [528, 375, 569, 477]}, {"id": "man.19", "bbox": [612, 376, 646, 467]}, {"id": "man.20", "bbox": [657, 382, 690, 476]}, {"id": "plant.21", "bbox": [462, 393, 532, 417]}, {"id": "shirt.22", "bbox": [612, 387, 646, 428]}, {"id": "tree.23", "bbox": [133, 67, 407, 439]}]
[{"subject": "man.19", "predicate": "wears", "object": "shirt.22"}, {"subject": "leaf.4", "predicate": "on", "object": "tree.3"}, {"subject": "leaf.10", "predicate": "on", "object": "tree.6"}, {"subject": "building.7", "predicate": "has", "object": "window.9"}, {"subject": "window.9", "predicate": "near", "object": "street.2"}, {"subject": "tree.3", "predicate": "has", "object": "leaf.4"}]
49
Generate a structured scene graph for an image of size (600 x 800) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (600 x 800) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 102, 549]}, {"id": "building.2", "bbox": [413, 0, 597, 799]}, {"id": "street.3", "bbox": [0, 594, 597, 796]}, {"id": "window.4", "bbox": [472, 10, 516, 288]}, {"id": "man.5", "bbox": [389, 564, 495, 787]}, {"id": "woman.6", "bbox": [75, 551, 167, 797]}, {"id": "man.7", "bbox": [275, 553, 353, 763]}, {"id": "vehicle.8", "bbox": [0, 584, 84, 673]}, {"id": "pole.9", "bbox": [225, 324, 266, 554]}, {"id": "jacket.10", "bbox": [276, 574, 356, 656]}, {"id": "bag.11", "bbox": [417, 594, 469, 692]}, {"id": "girl.12", "bbox": [369, 563, 403, 686]}, {"id": "bag.13", "bbox": [73, 592, 118, 688]}, {"id": "shirt.14", "bbox": [106, 587, 145, 669]}, {"id": "sign.15", "bbox": [225, 453, 289, 499]}, {"id": "coat.16", "bbox": [370, 578, 404, 638]}, {"id": "car.17", "bbox": [0, 550, 44, 578]}, {"id": "plant.18", "bbox": [151, 578, 183, 622]}, {"id": "bag.19", "bbox": [291, 581, 350, 676]}]
[{"subject": "girl.12", "predicate": "in", "object": "coat.16"}, {"subject": "woman.6", "predicate": "with", "object": "shirt.14"}, {"subject": "man.7", "predicate": "wearing", "object": "jacket.10"}, {"subject": "man.7", "predicate": "has", "object": "bag.19"}, {"subject": "woman.6", "predicate": "carrying", "object": "bag.13"}, {"subject": "man.5", "predicate": "carrying", "object": "bag.11"}, {"subject": "street.3", "predicate": "has", "object": "sign.15"}, {"subject": "sign.15", "predicate": "on", "object": "building.1"}, {"subject": "woman.6", "predicate": "has", "object": "bag.13"}, {"subject": "window.4", "predicate": "on", "object": "building.2"}]
50
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "paper.1", "bbox": [92, 39, 617, 334]}, {"id": "book.2", "bbox": [645, 217, 796, 564]}, {"id": "book.3", "bbox": [119, 96, 247, 256]}, {"id": "desk.4", "bbox": [192, 530, 455, 599]}, {"id": "book.5", "bbox": [301, 201, 471, 379]}, {"id": "book.6", "bbox": [396, 443, 492, 538]}, {"id": "book.7", "bbox": [200, 562, 455, 596]}, {"id": "table.8", "bbox": [0, 297, 754, 599]}, {"id": "cup.9", "bbox": [371, 380, 439, 460]}, {"id": "board.10", "bbox": [253, 61, 378, 164]}, {"id": "lamp.11", "bbox": [5, 103, 118, 388]}]
[{"subject": "book.2", "predicate": "on", "object": "desk.4"}, {"subject": "book.7", "predicate": "on", "object": "desk.4"}, {"subject": "book.6", "predicate": "on", "object": "desk.4"}, {"subject": "book.3", "predicate": "on", "object": "desk.4"}, {"subject": "book.7", "predicate": "on", "object": "desk.4"}, {"subject": "book.3", "predicate": "on", "object": "paper.1"}, {"subject": "paper.1", "predicate": "above", "object": "table.8"}, {"subject": "cup.9", "predicate": "above", "object": "table.8"}, {"subject": "lamp.11", "predicate": "above", "object": "table.8"}, {"subject": "book.5", "predicate": "above", "object": "table.8"}, {"subject": "paper.1", "predicate": "above", "object": "desk.4"}, {"subject": "book.5", "predicate": "above", "object": "desk.4"}, {"subject": "cup.9", "predicate": "above", "object": "table.8"}]
53
Generate a structured scene graph for an image of size (800 x 534) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 534) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [453, 1, 797, 332]}, {"id": "building.2", "bbox": [125, 0, 414, 328]}, {"id": "building.3", "bbox": [0, 0, 194, 479]}, {"id": "sidewalk.4", "bbox": [0, 361, 711, 531]}, {"id": "tree.5", "bbox": [307, 3, 467, 340]}, {"id": "car.6", "bbox": [546, 317, 756, 523]}, {"id": "street.7", "bbox": [110, 325, 751, 437]}, {"id": "phone.8", "bbox": [408, 182, 493, 439]}, {"id": "post.9", "bbox": [371, 325, 396, 381]}, {"id": "car.10", "bbox": [622, 300, 757, 355]}, {"id": "window.11", "bbox": [612, 90, 635, 214]}, {"id": "wheel.12", "bbox": [685, 445, 756, 520]}, {"id": "motorcycle.13", "bbox": [585, 306, 635, 346]}, {"id": "person.14", "bbox": [606, 292, 631, 344]}]
[{"subject": "building.1", "predicate": "has", "object": "window.11"}, {"subject": "building.1", "predicate": "has", "object": "window.11"}, {"subject": "person.14", "predicate": "riding", "object": "motorcycle.13"}, {"subject": "wheel.12", "predicate": "of", "object": "car.6"}, {"subject": "car.6", "predicate": "has", "object": "wheel.12"}, {"subject": "phone.8", "predicate": "on", "object": "sidewalk.4"}, {"subject": "building.1", "predicate": "has", "object": "window.11"}, {"subject": "person.14", "predicate": "on", "object": "motorcycle.13"}, {"subject": "car.10", "predicate": "near", "object": "motorcycle.13"}, {"subject": "phone.8", "predicate": "on", "object": "sidewalk.4"}, {"subject": "person.14", "predicate": "on", "object": "motorcycle.13"}]
54
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 636, 457]}, {"id": "car.2", "bbox": [39, 436, 396, 592]}, {"id": "fence.3", "bbox": [404, 379, 796, 460]}, {"id": "building.4", "bbox": [676, 157, 797, 381]}, {"id": "door.5", "bbox": [132, 440, 228, 561]}, {"id": "person.6", "bbox": [492, 408, 572, 574]}, {"id": "window.7", "bbox": [735, 221, 798, 385]}, {"id": "person.8", "bbox": [755, 404, 797, 567]}, {"id": "bike.9", "bbox": [561, 426, 671, 482]}, {"id": "pant.10", "bbox": [496, 489, 567, 571]}, {"id": "windshield.11", "bbox": [207, 446, 299, 490]}, {"id": "leg.12", "bbox": [524, 495, 571, 575]}, {"id": "jacket.13", "bbox": [501, 429, 551, 496]}, {"id": "person.14", "bbox": [526, 404, 564, 545]}, {"id": "wheel.15", "bbox": [244, 530, 303, 594]}, {"id": "pole.16", "bbox": [312, 223, 329, 487]}, {"id": "leg.17", "bbox": [765, 482, 789, 564]}, {"id": "leg.18", "bbox": [493, 500, 530, 567]}, {"id": "window.19", "bbox": [78, 442, 144, 487]}, {"id": "wheel.20", "bbox": [51, 514, 99, 569]}, {"id": "tire.21", "bbox": [626, 444, 672, 483]}, {"id": "window.22", "bbox": [195, 133, 221, 196]}, {"id": "leg.23", "bbox": [560, 500, 589, 555]}, {"id": "leg.24", "bbox": [600, 500, 625, 564]}, {"id": "window.25", "bbox": [197, 80, 224, 135]}, {"id": "shirt.26", "bbox": [578, 439, 612, 482]}, {"id": "window.27", "bbox": [418, 164, 432, 260]}, {"id": "person.28", "bbox": [560, 420, 624, 559]}]
[{"subject": "building.1", "predicate": "has", "object": "window.22"}, {"subject": "building.1", "predicate": "has", "object": "window.25"}, {"subject": "building.1", "predicate": "has", "object": "window.27"}, {"subject": "person.8", "predicate": "has", "object": "leg.17"}, {"subject": "person.28", "predicate": "has", "object": "leg.24"}, {"subject": "person.6", "predicate": "has", "object": "leg.12"}, {"subject": "person.6", "predicate": "has", "object": "leg.18"}, {"subject": "person.28", "predicate": "has", "object": "leg.23"}, {"subject": "car.2", "predicate": "has", "object": "wheel.15"}, {"subject": "car.2", "predicate": "has", "object": "wheel.20"}, {"subject": "car.2", "predicate": "has", "object": "windshield.11"}, {"subject": "car.2", "predicate": "has", "object": "window.19"}, {"subject": "person.28", "predicate": "has", "object": "leg.23"}, {"subject": "person.28", "predicate": "has", "object": "leg.24"}, {"subject": "building.4", "predicate": "has", "object": "window.7"}, {"subject": "bike.9", "predicate": "has", "object": "tire.21"}, {"subject": "person.28", "predicate": "wearing", "object": "shirt.26"}, {"subject": "person.6", "predicate": "wearing", "object": "jacket.13"}, {"subject": "car.2", "predicate": "has", "object": "door.5"}, {"subject": "person.6", "predicate": "wearing", "object": "pant.10"}]
55
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [662, 0, 800, 435]}, {"id": "building.2", "bbox": [180, 0, 758, 436]}, {"id": "building.3", "bbox": [0, 203, 187, 310]}, {"id": "street.4", "bbox": [0, 382, 799, 596]}, {"id": "wheel.5", "bbox": [7, 378, 140, 424]}, {"id": "pole.6", "bbox": [33, 0, 114, 598]}, {"id": "bus.7", "bbox": [0, 311, 275, 432]}, {"id": "sidewalk.8", "bbox": [246, 414, 796, 478]}, {"id": "flower.9", "bbox": [0, 0, 226, 192]}, {"id": "window.10", "bbox": [562, 200, 622, 278]}, {"id": "vehicle.11", "bbox": [745, 368, 795, 448]}, {"id": "windshield.12", "bbox": [185, 334, 267, 390]}, {"id": "window.13", "bbox": [562, 308, 619, 378]}, {"id": "window.14", "bbox": [560, 100, 615, 175]}, {"id": "window.15", "bbox": [496, 213, 528, 286]}, {"id": "bike.16", "bbox": [467, 384, 528, 425]}, {"id": "woman.17", "bbox": [432, 366, 460, 422]}, {"id": "window.18", "bbox": [493, 121, 521, 192]}, {"id": "person.19", "bbox": [442, 365, 463, 425]}, {"id": "person.20", "bbox": [336, 362, 361, 424]}, {"id": "person.21", "bbox": [288, 367, 311, 423]}, {"id": "window.22", "bbox": [447, 138, 470, 203]}, {"id": "bike.23", "bbox": [0, 440, 167, 595]}, {"id": "pole.24", "bbox": [10, 103, 156, 595]}]
[{"subject": "flower.9", "predicate": "from", "object": "pole.6"}, {"subject": "bike.23", "predicate": "near", "object": "pole.24"}, {"subject": "bus.7", "predicate": "on", "object": "street.4"}, {"subject": "bike.16", "predicate": "near", "object": "building.2"}, {"subject": "vehicle.11", "predicate": "on", "object": "sidewalk.8"}, {"subject": "window.14", "predicate": "in", "object": "building.2"}, {"subject": "flower.9", "predicate": "on", "object": "pole.6"}, {"subject": "wheel.5", "predicate": "under", "object": "bus.7"}, {"subject": "window.15", "predicate": "on", "object": "building.2"}, {"subject": "window.18", "predicate": "on", "object": "building.2"}, {"subject": "window.13", "predicate": "on", "object": "building.2"}, {"subject": "window.10", "predicate": "on", "object": "building.2"}, {"subject": "window.14", "predicate": "on", "object": "building.2"}, {"subject": "window.22", "predicate": "on", "object": "building.2"}]
56
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 200, 461]}, {"id": "building.2", "bbox": [421, 0, 715, 254]}, {"id": "window.3", "bbox": [96, 76, 176, 238]}, {"id": "window.4", "bbox": [453, 69, 533, 158]}, {"id": "window.5", "bbox": [235, 133, 280, 235]}, {"id": "window.6", "bbox": [328, 160, 367, 247]}, {"id": "window.7", "bbox": [550, 104, 575, 167]}, {"id": "window.8", "bbox": [585, 121, 608, 180]}, {"id": "building.9", "bbox": [0, 42, 417, 477]}]
[{"subject": "window.4", "predicate": "on", "object": "building.2"}, {"subject": "window.7", "predicate": "on", "object": "building.2"}, {"subject": "window.8", "predicate": "on", "object": "building.2"}, {"subject": "window.3", "predicate": "on", "object": "building.1"}, {"subject": "window.5", "predicate": "on", "object": "building.1"}, {"subject": "window.6", "predicate": "on", "object": "building.9"}, {"subject": "window.7", "predicate": "on", "object": "building.2"}, {"subject": "window.8", "predicate": "on", "object": "building.2"}]
57
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 358, 799, 596]}, {"id": "building.2", "bbox": [0, 0, 222, 410]}, {"id": "sidewalk.3", "bbox": [0, 357, 799, 462]}, {"id": "sign.4", "bbox": [28, 180, 217, 244]}, {"id": "car.5", "bbox": [373, 333, 579, 429]}, {"id": "man.6", "bbox": [431, 316, 515, 496]}, {"id": "people.7", "bbox": [261, 322, 296, 409]}]
[{"subject": "people.7", "predicate": "on", "object": "street.1"}, {"subject": "car.5", "predicate": "in", "object": "street.1"}, {"subject": "sign.4", "predicate": "hanging from", "object": "building.2"}]
58
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 335, 790, 594]}, {"id": "building.2", "bbox": [268, 0, 564, 444]}, {"id": "building.3", "bbox": [553, 0, 796, 491]}, {"id": "chair.4", "bbox": [261, 342, 524, 453]}, {"id": "window.5", "bbox": [112, 0, 275, 165]}, {"id": "table.6", "bbox": [303, 353, 487, 450]}, {"id": "window.7", "bbox": [736, 171, 795, 404]}, {"id": "man.8", "bbox": [605, 310, 669, 478]}, {"id": "sign.9", "bbox": [440, 104, 531, 178]}, {"id": "woman.10", "bbox": [200, 310, 256, 459]}, {"id": "pant.11", "bbox": [607, 381, 664, 471]}, {"id": "shirt.12", "bbox": [603, 332, 670, 389]}, {"id": "letter.13", "bbox": [444, 113, 524, 169]}, {"id": "guy.14", "bbox": [231, 303, 256, 361]}]
[{"subject": "sign.9", "predicate": "with", "object": "letter.13"}, {"subject": "man.8", "predicate": "wearing", "object": "shirt.12"}, {"subject": "chair.4", "predicate": "near", "object": "building.2"}]
59
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [350, 0, 796, 397]}, {"id": "street.2", "bbox": [0, 397, 680, 596]}, {"id": "car.3", "bbox": [362, 318, 628, 510]}, {"id": "board.4", "bbox": [432, 144, 615, 228]}, {"id": "man.5", "bbox": [736, 250, 799, 469]}, {"id": "sign.6", "bbox": [487, 0, 754, 53]}, {"id": "railing.7", "bbox": [192, 318, 417, 387]}, {"id": "truck.8", "bbox": [0, 239, 35, 425]}, {"id": "sign.9", "bbox": [685, 68, 751, 148]}, {"id": "shirt.10", "bbox": [735, 282, 796, 367]}, {"id": "car.11", "bbox": [42, 342, 138, 422]}, {"id": "motorcycle.12", "bbox": [557, 339, 741, 539]}, {"id": "light.13", "bbox": [425, 397, 477, 425]}, {"id": "sign.14", "bbox": [425, 139, 620, 227]}, {"id": "person.15", "bbox": [743, 250, 796, 471]}, {"id": "sign.16", "bbox": [346, 118, 428, 207]}, {"id": "sign.17", "bbox": [128, 242, 180, 267]}]
[{"subject": "truck.8", "predicate": "on", "object": "street.2"}, {"subject": "man.5", "predicate": "has", "object": "shirt.10"}, {"subject": "car.3", "predicate": "has", "object": "light.13"}, {"subject": "person.15", "predicate": "wearing", "object": "shirt.10"}, {"subject": "sign.16", "predicate": "on", "object": "building.1"}]
60
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [1, 241, 574, 597]}, {"id": "bike.2", "bbox": [160, 207, 287, 428]}, {"id": "pole.3", "bbox": [589, 0, 607, 600]}, {"id": "building.4", "bbox": [42, 5, 791, 597]}, {"id": "light.5", "bbox": [1, 14, 96, 85]}, {"id": "window.6", "bbox": [355, 0, 422, 129]}, {"id": "pole.7", "bbox": [319, 1, 332, 278]}, {"id": "tire.8", "bbox": [230, 339, 267, 430]}, {"id": "tire.9", "bbox": [389, 442, 444, 542]}, {"id": "seat.10", "bbox": [332, 342, 431, 374]}]
[{"subject": "pole.3", "predicate": "on", "object": "building.4"}, {"subject": "pole.7", "predicate": "on", "object": "building.4"}, {"subject": "tire.8", "predicate": "on", "object": "bike.2"}, {"subject": "pole.3", "predicate": "attached to", "object": "building.4"}, {"subject": "building.4", "predicate": "has", "object": "window.6"}]
62
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "window.1", "bbox": [328, 76, 377, 135]}, {"id": "building.2", "bbox": [303, 0, 616, 287]}, {"id": "window.3", "bbox": [527, 82, 577, 140]}, {"id": "window.4", "bbox": [430, 83, 478, 135]}, {"id": "window.5", "bbox": [532, 13, 576, 75]}, {"id": "window.6", "bbox": [429, 10, 475, 67]}, {"id": "window.7", "bbox": [329, 11, 374, 66]}, {"id": "window.8", "bbox": [209, 90, 264, 167]}, {"id": "building.9", "bbox": [4, 0, 307, 317]}, {"id": "window.10", "bbox": [125, 100, 175, 171]}, {"id": "window.11", "bbox": [220, 33, 259, 78]}, {"id": "window.12", "bbox": [160, 35, 205, 80]}, {"id": "window.13", "bbox": [108, 36, 147, 77]}, {"id": "window.14", "bbox": [532, 158, 568, 210]}, {"id": "car.15", "bbox": [157, 278, 709, 577]}, {"id": "car.16", "bbox": [120, 280, 232, 318]}, {"id": "street.17", "bbox": [0, 314, 794, 599]}, {"id": "car.18", "bbox": [637, 282, 728, 326]}, {"id": "sidewalk.19", "bbox": [0, 299, 321, 360]}, {"id": "woman.20", "bbox": [44, 269, 79, 358]}, {"id": "truck.21", "bbox": [725, 115, 796, 439]}, {"id": "plant.22", "bbox": [0, 322, 68, 421]}, {"id": "plant.23", "bbox": [217, 289, 277, 330]}, {"id": "plant.24", "bbox": [60, 297, 192, 371]}, {"id": "tire.25", "bbox": [628, 424, 699, 512]}, {"id": "tire.26", "bbox": [267, 461, 380, 571]}, {"id": "person.27", "bbox": [322, 271, 343, 318]}, {"id": "window.28", "bbox": [432, 157, 467, 205]}]
[{"subject": "window.1", "predicate": "on", "object": "building.2"}, {"subject": "window.3", "predicate": "on", "object": "building.2"}, {"subject": "window.4", "predicate": "on", "object": "building.2"}, {"subject": "window.5", "predicate": "on", "object": "building.2"}, {"subject": "window.6", "predicate": "on", "object": "building.2"}, {"subject": "window.7", "predicate": "on", "object": "building.2"}, {"subject": "window.8", "predicate": "on", "object": "building.9"}, {"subject": "window.10", "predicate": "on", "object": "building.9"}, {"subject": "window.11", "predicate": "on", "object": "building.9"}, {"subject": "window.12", "predicate": "on", "object": "building.9"}, {"subject": "window.13", "predicate": "on", "object": "building.9"}, {"subject": "window.4", "predicate": "on", "object": "building.2"}, {"subject": "window.8", "predicate": "on", "object": "building.9"}, {"subject": "window.10", "predicate": "on", "object": "building.9"}, {"subject": "window.14", "predicate": "on", "object": "building.2"}, {"subject": "window.6", "predicate": "on", "object": "building.2"}, {"subject": "window.5", "predicate": "on", "object": "building.2"}, {"subject": "window.7", "predicate": "on", "object": "building.2"}, {"subject": "window.11", "predicate": "on", "object": "building.9"}, {"subject": "window.13", "predicate": "on", "object": "building.9"}, {"subject": "window.13", "predicate": "on", "object": "building.9"}, {"subject": "car.16", "predicate": "on", "object": "street.17"}, {"subject": "car.18", "predicate": "on", "object": "street.17"}, {"subject": "car.15", "predicate": "on", "object": "street.17"}, {"subject": "truck.21", "predicate": "behind", "object": "car.15"}, {"subject": "window.28", "predicate": "on", "object": "building.2"}, {"subject": "window.28", "predicate": "on", "object": "building.9"}]
63
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 125, 794, 592]}, {"id": "counter.2", "bbox": [0, 0, 796, 277]}, {"id": "plate.3", "bbox": [467, 260, 772, 540]}, {"id": "plate.4", "bbox": [29, 176, 256, 336]}, {"id": "plate.5", "bbox": [310, 131, 530, 273]}, {"id": "handle.6", "bbox": [55, 452, 93, 552]}, {"id": "handle.7", "bbox": [418, 380, 485, 551]}, {"id": "handle.8", "bbox": [286, 375, 357, 507]}, {"id": "fork.9", "bbox": [297, 299, 339, 496]}, {"id": "handle.10", "bbox": [271, 217, 290, 281]}, {"id": "fork.11", "bbox": [263, 164, 305, 291]}, {"id": "handle.12", "bbox": [296, 392, 337, 477]}]
[{"subject": "fork.9", "predicate": "on", "object": "table.1"}, {"subject": "fork.11", "predicate": "on", "object": "table.1"}, {"subject": "fork.9", "predicate": "on", "object": "table.1"}, {"subject": "plate.4", "predicate": "on", "object": "table.1"}, {"subject": "plate.5", "predicate": "on", "object": "table.1"}, {"subject": "plate.3", "predicate": "on", "object": "table.1"}, {"subject": "plate.3", "predicate": "on", "object": "table.1"}, {"subject": "fork.9", "predicate": "has", "object": "handle.12"}, {"subject": "fork.11", "predicate": "on", "object": "table.1"}, {"subject": "plate.4", "predicate": "on", "object": "table.1"}, {"subject": "handle.8", "predicate": "on", "object": "fork.9"}, {"subject": "plate.4", "predicate": "on", "object": "table.1"}, {"subject": "fork.11", "predicate": "with", "object": "handle.8"}, {"subject": "plate.4", "predicate": "with", "object": "plate.3"}, {"subject": "plate.5", "predicate": "with", "object": "plate.3"}, {"subject": "fork.9", "predicate": "with", "object": "handle.8"}, {"subject": "table.1", "predicate": "with", "object": "plate.5"}]
64
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [375, 315, 501, 400]}, {"id": "woman.2", "bbox": [389, 405, 432, 504]}, {"id": "jacket.3", "bbox": [390, 425, 424, 469]}, {"id": "building.4", "bbox": [462, 10, 710, 343]}, {"id": "post.5", "bbox": [549, 340, 562, 445]}, {"id": "man.6", "bbox": [728, 389, 755, 449]}, {"id": "pole.7", "bbox": [50, 0, 122, 560]}, {"id": "sidewalk.8", "bbox": [290, 486, 521, 525]}, {"id": "building.9", "bbox": [313, 186, 416, 352]}, {"id": "street.10", "bbox": [358, 547, 729, 593]}, {"id": "window.11", "bbox": [549, 36, 606, 70]}, {"id": "person.12", "bbox": [658, 393, 679, 449]}, {"id": "building.13", "bbox": [129, 214, 468, 337]}]
[{"subject": "woman.2", "predicate": "wearing", "object": "jacket.3"}, {"subject": "woman.2", "predicate": "on", "object": "sidewalk.8"}, {"subject": "pole.7", "predicate": "near", "object": "street.10"}, {"subject": "building.4", "predicate": "has", "object": "window.11"}, {"subject": "woman.2", "predicate": "wearing", "object": "jacket.3"}]
65
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "car.1", "bbox": [0, 0, 798, 599]}, {"id": "car.2", "bbox": [468, 80, 798, 328]}, {"id": "sidewalk.3", "bbox": [0, 304, 286, 596]}, {"id": "window.4", "bbox": [257, 160, 502, 284]}, {"id": "man.5", "bbox": [367, 50, 497, 239]}, {"id": "tire.6", "bbox": [268, 436, 349, 550]}, {"id": "truck.7", "bbox": [179, 75, 325, 148]}, {"id": "plate.8", "bbox": [457, 400, 592, 474]}, {"id": "light.9", "bbox": [350, 383, 420, 447]}, {"id": "hair.10", "bbox": [411, 62, 450, 104]}, {"id": "sign.11", "bbox": [281, 0, 320, 29]}]
[{"subject": "man.5", "predicate": "with", "object": "hair.10"}]
69
Generate a structured scene graph for an image of size (640 x 480) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (640 x 480) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 0, 639, 479]}, {"id": "plate.2", "bbox": [185, 26, 557, 401]}]
[{"subject": "table.1", "predicate": "under", "object": "plate.2"}]
70
Generate a structured scene graph for an image of size (600 x 446) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (600 x 446) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 599, 445]}, {"id": "chair.2", "bbox": [66, 280, 592, 442]}, {"id": "jacket.3", "bbox": [446, 142, 489, 197]}, {"id": "woman.4", "bbox": [48, 208, 164, 319]}, {"id": "man.5", "bbox": [401, 118, 444, 274]}, {"id": "man.6", "bbox": [364, 127, 401, 247]}, {"id": "woman.7", "bbox": [263, 181, 364, 292]}, {"id": "man.8", "bbox": [181, 185, 305, 306]}, {"id": "jacket.9", "bbox": [50, 253, 166, 319]}, {"id": "chair.10", "bbox": [139, 242, 215, 294]}, {"id": "hand.11", "bbox": [268, 273, 306, 297]}, {"id": "woman.12", "bbox": [483, 148, 515, 247]}, {"id": "shirt.13", "bbox": [485, 162, 515, 201]}, {"id": "paper.14", "bbox": [420, 202, 445, 233]}, {"id": "bag.15", "bbox": [442, 200, 460, 238]}, {"id": "person.16", "bbox": [216, 191, 303, 241]}, {"id": "man.17", "bbox": [450, 109, 493, 267]}, {"id": "men.18", "bbox": [329, 98, 490, 243]}]
[{"subject": "person.16", "predicate": "in", "object": "chair.10"}, {"subject": "person.16", "predicate": "in", "object": "chair.10"}, {"subject": "person.16", "predicate": "on", "object": "chair.10"}, {"subject": "man.5", "predicate": "wearing", "object": "jacket.3"}, {"subject": "woman.12", "predicate": "wearing", "object": "shirt.13"}, {"subject": "man.8", "predicate": "in", "object": "shirt.13"}, {"subject": "man.8", "predicate": "looking at", "object": "hand.11"}, {"subject": "man.6", "predicate": "holding", "object": "bag.15"}, {"subject": "man.8", "predicate": "at", "object": "hand.11"}, {"subject": "man.5", "predicate": "holding", "object": "paper.14"}]
71
Generate a structured scene graph for an image of size (500 x 375) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (500 x 375) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "woman.1", "bbox": [196, 153, 337, 371]}, {"id": "desk.2", "bbox": [197, 263, 434, 373]}, {"id": "light.3", "bbox": [20, 0, 284, 88]}, {"id": "woman.4", "bbox": [131, 138, 218, 373]}, {"id": "table.5", "bbox": [124, 221, 212, 372]}, {"id": "woman.6", "bbox": [302, 132, 447, 228]}, {"id": "shirt.7", "bbox": [195, 207, 303, 295]}, {"id": "jacket.8", "bbox": [327, 165, 446, 222]}, {"id": "box.9", "bbox": [312, 222, 419, 291]}, {"id": "jean.10", "bbox": [206, 317, 341, 373]}, {"id": "pant.11", "bbox": [147, 270, 215, 373]}, {"id": "hair.12", "bbox": [211, 156, 287, 241]}, {"id": "jacket.13", "bbox": [129, 178, 216, 234]}, {"id": "hair.14", "bbox": [150, 139, 192, 180]}, {"id": "bowl.15", "bbox": [300, 275, 337, 298]}, {"id": "hand.16", "bbox": [303, 200, 333, 224]}, {"id": "desk.17", "bbox": [124, 233, 222, 372]}]
[{"subject": "woman.4", "predicate": "sitting on", "object": "desk.17"}, {"subject": "woman.4", "predicate": "wearing", "object": "pant.11"}, {"subject": "woman.6", "predicate": "has", "object": "hand.16"}, {"subject": "woman.4", "predicate": "at", "object": "table.5"}, {"subject": "bowl.15", "predicate": "sitting on", "object": "desk.2"}]
72
Generate a structured scene graph for an image of size (495 x 247) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (495 x 247) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "people.1", "bbox": [0, 0, 48, 35]}, {"id": "table.2", "bbox": [0, 114, 432, 245]}, {"id": "person.3", "bbox": [38, 12, 145, 134]}, {"id": "person.4", "bbox": [169, 13, 250, 114]}, {"id": "person.5", "bbox": [233, 5, 334, 126]}, {"id": "woman.6", "bbox": [425, 65, 493, 240]}, {"id": "woman.7", "bbox": [0, 18, 70, 165]}, {"id": "shirt.8", "bbox": [38, 52, 144, 123]}, {"id": "jacket.9", "bbox": [246, 37, 329, 122]}, {"id": "head.10", "bbox": [197, 13, 238, 55]}, {"id": "head.11", "bbox": [339, 25, 381, 76]}, {"id": "head.12", "bbox": [81, 13, 121, 60]}, {"id": "glass.13", "bbox": [93, 149, 120, 223]}, {"id": "woman.14", "bbox": [450, 120, 492, 245]}, {"id": "head.15", "bbox": [263, 4, 298, 46]}, {"id": "head.16", "bbox": [0, 17, 41, 77]}, {"id": "hair.17", "bbox": [0, 14, 40, 53]}, {"id": "hair.18", "bbox": [198, 13, 238, 43]}, {"id": "shirt.19", "bbox": [0, 75, 39, 164]}, {"id": "glass.20", "bbox": [430, 105, 481, 124]}, {"id": "glass.21", "bbox": [86, 30, 118, 41]}, {"id": "arm.22", "bbox": [228, 57, 251, 91]}, {"id": "glass.23", "bbox": [292, 104, 311, 146]}, {"id": "man.24", "bbox": [262, 8, 325, 120]}, {"id": "woman.25", "bbox": [168, 11, 243, 118]}, {"id": "man.26", "bbox": [55, 14, 144, 124]}, {"id": "person.27", "bbox": [330, 15, 426, 155]}, {"id": "person.28", "bbox": [2, 12, 44, 153]}]
[{"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "glass.13", "predicate": "near", "object": "man.24"}, {"subject": "glass.21", "predicate": "on", "object": "man.26"}, {"subject": "people.1", "predicate": "at", "object": "table.2"}, {"subject": "glass.20", "predicate": "on", "object": "woman.6"}, {"subject": "woman.7", "predicate": "in", "object": "shirt.19"}, {"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "glass.13", "predicate": "on", "object": "table.2"}, {"subject": "woman.6", "predicate": "wearing", "object": "glass.20"}, {"subject": "man.24", "predicate": "has", "object": "arm.22"}, {"subject": "person.27", "predicate": "has", "object": "head.11"}, {"subject": "person.5", "predicate": "has", "object": "head.15"}, {"subject": "person.4", "predicate": "has", "object": "head.10"}, {"subject": "head.12", "predicate": "of", "object": "person.3"}, {"subject": "person.28", "predicate": "with", "object": "head.16"}]
73
Generate a structured scene graph for an image of size (800 x 533) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 533) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "leg.1", "bbox": [707, 339, 729, 442]}, {"id": "chair.2", "bbox": [660, 239, 753, 463]}, {"id": "man.3", "bbox": [35, 130, 239, 339]}, {"id": "glass.4", "bbox": [114, 157, 164, 180]}, {"id": "woman.5", "bbox": [198, 119, 314, 285]}, {"id": "shirt.6", "bbox": [200, 182, 316, 299]}, {"id": "woman.7", "bbox": [242, 171, 492, 518]}, {"id": "chair.8", "bbox": [267, 400, 479, 531]}, {"id": "man.9", "bbox": [467, 131, 607, 443]}, {"id": "chair.10", "bbox": [459, 348, 575, 529]}, {"id": "woman.11", "bbox": [583, 132, 681, 391]}, {"id": "chair.12", "bbox": [558, 260, 689, 517]}, {"id": "bottle.13", "bbox": [207, 250, 257, 343]}, {"id": "hand.14", "bbox": [338, 380, 389, 462]}, {"id": "glass.15", "bbox": [132, 292, 166, 352]}, {"id": "hair.16", "bbox": [482, 134, 562, 173]}, {"id": "hair.17", "bbox": [620, 133, 671, 183]}, {"id": "table.18", "bbox": [0, 129, 796, 514]}, {"id": "hair.19", "bbox": [0, 351, 57, 520]}, {"id": "tie.20", "bbox": [472, 222, 525, 331]}, {"id": "shirt.21", "bbox": [110, 192, 166, 305]}, {"id": "jacket.22", "bbox": [300, 267, 504, 529]}, {"id": "fork.23", "bbox": [214, 355, 255, 387]}]
[{"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "leg.1", "predicate": "of", "object": "chair.2"}, {"subject": "man.3", "predicate": "wearing", "object": "glass.4"}, {"subject": "woman.5", "predicate": "in", "object": "shirt.6"}, {"subject": "woman.7", "predicate": "sitting on", "object": "chair.8"}, {"subject": "man.9", "predicate": "sitting on", "object": "chair.10"}, {"subject": "woman.11", "predicate": "sitting on", "object": "chair.12"}, {"subject": "hand.14", "predicate": "of", "object": "woman.7"}, {"subject": "woman.5", "predicate": "wearing", "object": "shirt.6"}, {"subject": "man.9", "predicate": "with", "object": "hair.16"}, {"subject": "woman.11", "predicate": "with", "object": "hair.17"}, {"subject": "man.3", "predicate": "wearing", "object": "glass.4"}, {"subject": "table.18", "predicate": "in front of", "object": "woman.7"}, {"subject": "hair.19", "predicate": "at", "object": "table.18"}, {"subject": "man.9", "predicate": "wearing", "object": "tie.20"}, {"subject": "woman.11", "predicate": "with", "object": "hair.17"}, {"subject": "woman.5", "predicate": "in", "object": "shirt.6"}, {"subject": "glass.15", "predicate": "on", "object": "table.18"}, {"subject": "man.3", "predicate": "wearing", "object": "shirt.21"}, {"subject": "bottle.13", "predicate": "on", "object": "table.18"}, {"subject": "woman.11", "predicate": "with", "object": "hair.17"}, {"subject": "man.9", "predicate": "with", "object": "hair.16"}, {"subject": "woman.5", "predicate": "wearing", "object": "shirt.6"}, {"subject": "man.3", "predicate": "with", "object": "glass.4"}, {"subject": "woman.7", "predicate": "with", "object": "jacket.22"}, {"subject": "fork.23", "predicate": "in front of", "object": "woman.7"}]
74
Generate a structured scene graph for an image of size (800 x 599) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 599) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "screen.1", "bbox": [511, 19, 756, 314]}, {"id": "table.2", "bbox": [408, 418, 703, 596]}, {"id": "shelf.3", "bbox": [489, 308, 755, 500]}, {"id": "table.4", "bbox": [217, 375, 425, 543]}, {"id": "chair.5", "bbox": [437, 483, 601, 597]}, {"id": "sign.6", "bbox": [283, 78, 464, 189]}, {"id": "chair.7", "bbox": [354, 410, 457, 597]}, {"id": "chair.8", "bbox": [489, 392, 602, 550]}, {"id": "chair.9", "bbox": [200, 393, 314, 548]}, {"id": "chair.10", "bbox": [219, 365, 314, 492]}, {"id": "chair.11", "bbox": [364, 362, 446, 492]}, {"id": "man.12", "bbox": [516, 96, 586, 221]}, {"id": "woman.13", "bbox": [596, 92, 663, 213]}, {"id": "leaf.14", "bbox": [44, 308, 103, 417]}, {"id": "light.15", "bbox": [47, 11, 93, 132]}, {"id": "light.16", "bbox": [586, 0, 639, 95]}, {"id": "counter.17", "bbox": [48, 290, 289, 309]}, {"id": "light.18", "bbox": [332, 46, 370, 149]}, {"id": "light.19", "bbox": [181, 0, 251, 44]}, {"id": "light.20", "bbox": [0, 82, 28, 169]}, {"id": "light.21", "bbox": [206, 101, 237, 181]}, {"id": "people.22", "bbox": [516, 97, 669, 221]}]
[{"subject": "chair.9", "predicate": "near", "object": "table.4"}, {"subject": "chair.5", "predicate": "near", "object": "table.2"}, {"subject": "chair.7", "predicate": "near", "object": "table.2"}, {"subject": "chair.10", "predicate": "near", "object": "table.4"}, {"subject": "chair.11", "predicate": "near", "object": "table.4"}, {"subject": "people.22", "predicate": "on", "object": "screen.1"}, {"subject": "woman.13", "predicate": "on", "object": "screen.1"}, {"subject": "chair.8", "predicate": "near", "object": "table.2"}]
77
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 799, 595]}, {"id": "table.2", "bbox": [324, 404, 795, 595]}, {"id": "table.3", "bbox": [0, 248, 698, 435]}, {"id": "chair.4", "bbox": [72, 415, 243, 597]}, {"id": "man.5", "bbox": [600, 286, 789, 475]}, {"id": "light.6", "bbox": [480, 329, 796, 436]}, {"id": "window.7", "bbox": [692, 1, 797, 315]}, {"id": "chair.8", "bbox": [240, 382, 393, 582]}, {"id": "man.9", "bbox": [497, 264, 628, 411]}, {"id": "chair.10", "bbox": [460, 375, 598, 460]}, {"id": "man.11", "bbox": [650, 253, 733, 349]}, {"id": "head.12", "bbox": [682, 300, 742, 378]}, {"id": "wire.13", "bbox": [15, 430, 78, 525]}, {"id": "chair.14", "bbox": [578, 335, 635, 427]}, {"id": "screen.15", "bbox": [35, 289, 114, 347]}, {"id": "shirt.16", "bbox": [549, 293, 607, 350]}, {"id": "screen.17", "bbox": [202, 276, 267, 329]}, {"id": "hair.18", "bbox": [680, 303, 750, 353]}, {"id": "head.19", "bbox": [532, 267, 570, 303]}, {"id": "screen.20", "bbox": [367, 267, 412, 315]}, {"id": "sign.21", "bbox": [381, 160, 418, 217]}, {"id": "head.22", "bbox": [668, 253, 705, 286]}, {"id": "screen.23", "bbox": [469, 261, 506, 303]}, {"id": "screen.24", "bbox": [553, 257, 580, 295]}, {"id": "chair.25", "bbox": [612, 321, 692, 365]}, {"id": "desk.26", "bbox": [18, 289, 680, 389]}, {"id": "shirt.27", "bbox": [613, 352, 789, 464]}, {"id": "building.28", "bbox": [0, 0, 794, 435]}]
[{"subject": "man.9", "predicate": "wearing", "object": "shirt.16"}, {"subject": "man.5", "predicate": "wearing", "object": "shirt.27"}, {"subject": "man.5", "predicate": "in", "object": "room.1"}, {"subject": "window.7", "predicate": "on", "object": "building.28"}]
79
Generate a structured scene graph for an image of size (600 x 450) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (600 x 450) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 285, 278, 447]}, {"id": "light.2", "bbox": [8, 0, 121, 126]}, {"id": "window.3", "bbox": [220, 106, 326, 241]}, {"id": "chair.4", "bbox": [100, 323, 193, 447]}, {"id": "chair.5", "bbox": [246, 281, 311, 449]}, {"id": "chair.6", "bbox": [186, 298, 248, 448]}, {"id": "curtain.7", "bbox": [219, 94, 259, 264]}, {"id": "lamp.8", "bbox": [246, 166, 299, 270]}]
[{"subject": "light.2", "predicate": "over", "object": "table.1"}, {"subject": "table.1", "predicate": "and", "object": "chair.5"}, {"subject": "window.3", "predicate": "with", "object": "curtain.7"}, {"subject": "chair.5", "predicate": "under", "object": "table.1"}, {"subject": "chair.5", "predicate": "under", "object": "table.1"}, {"subject": "chair.5", "predicate": "under", "object": "table.1"}, {"subject": "chair.5", "predicate": "under", "object": "table.1"}, {"subject": "chair.5", "predicate": "at", "object": "table.1"}]
81
Generate a structured scene graph for an image of size (352 x 512) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (352 x 512) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "cabinet.1", "bbox": [0, 0, 51, 189]}, {"id": "cabinet.2", "bbox": [0, 349, 70, 510]}, {"id": "cabinet.3", "bbox": [116, 38, 184, 174]}, {"id": "door.4", "bbox": [0, 401, 71, 510]}, {"id": "cabinet.5", "bbox": [145, 276, 177, 440]}, {"id": "door.6", "bbox": [145, 311, 175, 441]}, {"id": "cabinet.7", "bbox": [197, 248, 225, 371]}, {"id": "cabinet.8", "bbox": [174, 265, 197, 401]}, {"id": "door.9", "bbox": [198, 270, 225, 373]}, {"id": "door.10", "bbox": [174, 290, 199, 402]}, {"id": "door.11", "bbox": [162, 57, 185, 170]}, {"id": "cabinet.12", "bbox": [215, 83, 227, 167]}, {"id": "cabinet.13", "bbox": [184, 67, 201, 127]}, {"id": "door.14", "bbox": [200, 75, 215, 128]}, {"id": "door.15", "bbox": [213, 80, 228, 166]}, {"id": "cabinet.16", "bbox": [198, 69, 217, 130]}, {"id": "door.17", "bbox": [2, 0, 52, 181]}, {"id": "door.18", "bbox": [183, 66, 202, 126]}]
[{"subject": "door.15", "predicate": "on", "object": "cabinet.12"}, {"subject": "door.14", "predicate": "on", "object": "cabinet.16"}, {"subject": "door.4", "predicate": "on", "object": "cabinet.2"}, {"subject": "door.6", "predicate": "on", "object": "cabinet.5"}, {"subject": "door.10", "predicate": "on", "object": "cabinet.8"}, {"subject": "door.9", "predicate": "for", "object": "cabinet.7"}, {"subject": "door.17", "predicate": "for", "object": "cabinet.1"}, {"subject": "door.11", "predicate": "for", "object": "cabinet.3"}, {"subject": "door.18", "predicate": "for", "object": "cabinet.13"}]
82
Generate a structured scene graph for an image of size (400 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (400 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "cabinet.1", "bbox": [2, 18, 383, 288]}, {"id": "counter.2", "bbox": [214, 312, 372, 556]}, {"id": "door.3", "bbox": [357, 142, 398, 530]}, {"id": "counter.4", "bbox": [0, 308, 228, 350]}, {"id": "door.5", "bbox": [355, 497, 397, 595]}, {"id": "sink.6", "bbox": [264, 346, 370, 380]}, {"id": "tile.7", "bbox": [4, 495, 266, 596]}]
[{"subject": "cabinet.1", "predicate": "above", "object": "counter.2"}, {"subject": "sink.6", "predicate": "on", "object": "counter.2"}]
83
Generate a structured scene graph for an image of size (800 x 534) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 534) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "book.1", "bbox": [276, 293, 440, 412]}, {"id": "lamp.2", "bbox": [712, 114, 799, 238]}, {"id": "girl.3", "bbox": [487, 100, 561, 200]}, {"id": "man.4", "bbox": [188, 96, 266, 187]}, {"id": "book.5", "bbox": [120, 285, 273, 350]}, {"id": "person.6", "bbox": [364, 100, 459, 223]}, {"id": "chair.7", "bbox": [103, 169, 271, 196]}, {"id": "table.8", "bbox": [0, 300, 799, 532]}, {"id": "tower.9", "bbox": [612, 111, 646, 160]}, {"id": "shirt.10", "bbox": [500, 125, 564, 188]}, {"id": "woman.11", "bbox": [485, 105, 550, 150]}]
[{"subject": "book.1", "predicate": "above", "object": "table.8"}, {"subject": "tower.9", "predicate": "above", "object": "table.8"}, {"subject": "lamp.2", "predicate": "on", "object": "table.8"}, {"subject": "chair.7", "predicate": "at", "object": "table.8"}]
84
Generate a structured scene graph for an image of size (645 x 800) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (645 x 800) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "door.1", "bbox": [139, 72, 470, 516]}, {"id": "seat.2", "bbox": [239, 385, 641, 656]}, {"id": "table.3", "bbox": [0, 421, 277, 767]}, {"id": "seat.4", "bbox": [329, 550, 641, 795]}, {"id": "lamp.5", "bbox": [85, 129, 270, 441]}, {"id": "flower.6", "bbox": [49, 208, 232, 417]}, {"id": "door.7", "bbox": [364, 68, 476, 389]}, {"id": "curtain.8", "bbox": [58, 15, 154, 446]}, {"id": "book.9", "bbox": [49, 543, 179, 563]}, {"id": "pillow.10", "bbox": [294, 414, 479, 496]}, {"id": "book.11", "bbox": [61, 686, 171, 716]}, {"id": "vase.12", "bbox": [105, 380, 172, 457]}, {"id": "book.13", "bbox": [60, 676, 185, 708]}, {"id": "book.14", "bbox": [1, 350, 58, 478]}, {"id": "shelf.15", "bbox": [33, 583, 182, 624]}, {"id": "glass.16", "bbox": [510, 500, 562, 581]}, {"id": "glass.17", "bbox": [430, 490, 480, 568]}, {"id": "book.18", "bbox": [39, 451, 130, 471]}, {"id": "book.19", "bbox": [47, 588, 171, 617]}, {"id": "book.20", "bbox": [45, 580, 173, 607]}, {"id": "book.21", "bbox": [53, 571, 185, 601]}, {"id": "book.22", "bbox": [58, 655, 197, 700]}, {"id": "book.23", "bbox": [46, 555, 178, 582]}, {"id": "book.24", "bbox": [58, 535, 178, 557]}, {"id": "shelf.25", "bbox": [43, 579, 192, 614]}]
[{"subject": "flower.6", "predicate": "in", "object": "vase.12"}, {"subject": "shelf.25", "predicate": "under", "object": "table.3"}, {"subject": "book.9", "predicate": "on", "object": "shelf.15"}, {"subject": "book.24", "predicate": "on", "object": "shelf.15"}, {"subject": "book.20", "predicate": "on", "object": "shelf.15"}, {"subject": "book.21", "predicate": "on", "object": "shelf.15"}, {"subject": "book.23", "predicate": "on", "object": "shelf.15"}, {"subject": "vase.12", "predicate": "in front of", "object": "lamp.5"}, {"subject": "flower.6", "predicate": "in", "object": "vase.12"}, {"subject": "curtain.8", "predicate": "behind", "object": "lamp.5"}, {"subject": "pillow.10", "predicate": "on", "object": "seat.2"}]
85
Generate a structured scene graph for an image of size (800 x 535) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 535) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "cabinet.1", "bbox": [0, 220, 311, 532]}, {"id": "table.2", "bbox": [475, 312, 646, 532]}, {"id": "chair.3", "bbox": [143, 254, 310, 446]}, {"id": "cabinet.4", "bbox": [0, 317, 76, 533]}, {"id": "flower.5", "bbox": [25, 139, 103, 258]}, {"id": "screen.6", "bbox": [423, 197, 510, 266]}, {"id": "lamp.7", "bbox": [633, 187, 697, 282]}, {"id": "table.8", "bbox": [611, 265, 706, 320]}, {"id": "pot.9", "bbox": [51, 250, 92, 285]}, {"id": "lamp.10", "bbox": [646, 238, 679, 291]}]
[{"subject": "flower.5", "predicate": "in", "object": "pot.9"}, {"subject": "lamp.7", "predicate": "on", "object": "table.8"}]
86
Generate a structured scene graph for an image of size (550 x 372) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (550 x 372) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "person.1", "bbox": [209, 168, 273, 281]}, {"id": "train.2", "bbox": [42, 3, 493, 356]}, {"id": "man.3", "bbox": [168, 165, 261, 319]}, {"id": "person.4", "bbox": [66, 180, 176, 359]}, {"id": "man.5", "bbox": [179, 166, 264, 315]}, {"id": "person.6", "bbox": [77, 165, 179, 257]}, {"id": "shoe.7", "bbox": [233, 288, 261, 320]}, {"id": "shoe.8", "bbox": [218, 283, 265, 315]}, {"id": "window.9", "bbox": [73, 75, 189, 188]}, {"id": "woman.10", "bbox": [92, 172, 191, 340]}, {"id": "pant.11", "bbox": [194, 230, 249, 306]}, {"id": "sign.12", "bbox": [19, 84, 49, 121]}, {"id": "man.13", "bbox": [387, 27, 514, 368]}, {"id": "book.14", "bbox": [475, 76, 533, 122]}, {"id": "person.15", "bbox": [100, 172, 189, 337]}, {"id": "hat.16", "bbox": [105, 165, 152, 194]}, {"id": "person.17", "bbox": [100, 166, 187, 272]}]
[{"subject": "person.1", "predicate": "sitting on", "object": "train.2"}, {"subject": "man.3", "predicate": "sitting on", "object": "train.2"}, {"subject": "person.4", "predicate": "sitting on", "object": "train.2"}, {"subject": "person.1", "predicate": "sitting on", "object": "train.2"}, {"subject": "man.3", "predicate": "sitting on", "object": "train.2"}, {"subject": "person.6", "predicate": "sitting on", "object": "train.2"}, {"subject": "person.4", "predicate": "sitting on", "object": "train.2"}, {"subject": "window.9", "predicate": "behind", "object": "woman.10"}, {"subject": "man.3", "predicate": "wearing", "object": "pant.11"}, {"subject": "person.15", "predicate": "wearing", "object": "hat.16"}, {"subject": "person.17", "predicate": "wearing", "object": "hat.16"}]
87
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [0, 0, 799, 344]}, {"id": "rock.2", "bbox": [372, 296, 796, 382]}, {"id": "rock.3", "bbox": [143, 342, 413, 375]}, {"id": "man.4", "bbox": [274, 308, 315, 375]}, {"id": "man.5", "bbox": [90, 285, 115, 328]}]
[{"subject": "man.4", "predicate": "sitting on", "object": "rock.3"}]
88
Generate a structured scene graph for an image of size (512 x 340) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (512 x 340) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "lamp.1", "bbox": [252, 119, 283, 170]}, {"id": "table.2", "bbox": [224, 154, 303, 246]}, {"id": "pillow.3", "bbox": [54, 217, 108, 267]}, {"id": "chair.4", "bbox": [28, 186, 163, 337]}, {"id": "shelf.5", "bbox": [121, 0, 189, 198]}, {"id": "shelf.6", "bbox": [141, 91, 178, 104]}, {"id": "shelf.7", "bbox": [142, 67, 172, 84]}, {"id": "shelf.8", "bbox": [131, 11, 170, 27]}, {"id": "window.9", "bbox": [199, 0, 301, 174]}, {"id": "chair.10", "bbox": [132, 159, 238, 260]}, {"id": "window.11", "bbox": [194, 4, 309, 187]}, {"id": "pillow.12", "bbox": [263, 151, 398, 212]}, {"id": "book.13", "bbox": [134, 7, 177, 159]}, {"id": "lamp.14", "bbox": [255, 114, 286, 181]}]
[{"subject": "pillow.3", "predicate": "on", "object": "chair.4"}, {"subject": "window.9", "predicate": "on", "object": "window.11"}, {"subject": "pillow.3", "predicate": "on", "object": "chair.4"}, {"subject": "table.2", "predicate": "in front of", "object": "window.11"}]
89
Generate a structured scene graph for an image of size (800 x 533) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 533) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "chair.1", "bbox": [0, 362, 395, 531]}, {"id": "table.2", "bbox": [0, 204, 200, 346]}, {"id": "lamp.3", "bbox": [339, 50, 441, 310]}, {"id": "door.4", "bbox": [73, 217, 200, 327]}, {"id": "flower.5", "bbox": [227, 104, 305, 225]}, {"id": "door.6", "bbox": [0, 239, 78, 345]}, {"id": "vase.7", "bbox": [255, 176, 294, 228]}, {"id": "leg.8", "bbox": [443, 263, 477, 361]}, {"id": "shelf.9", "bbox": [220, 282, 326, 324]}, {"id": "lamp.10", "bbox": [0, 142, 44, 240]}, {"id": "shelf.11", "bbox": [220, 242, 323, 277]}, {"id": "shelf.12", "bbox": [229, 214, 304, 236]}, {"id": "lamp.13", "bbox": [20, 235, 107, 356]}, {"id": "flower.14", "bbox": [234, 126, 337, 192]}, {"id": "table.15", "bbox": [221, 211, 328, 316]}]
[{"subject": "flower.14", "predicate": "in", "object": "vase.7"}, {"subject": "flower.5", "predicate": "in", "object": "vase.7"}, {"subject": "lamp.10", "predicate": "on", "object": "table.2"}, {"subject": "shelf.12", "predicate": "on", "object": "table.15"}, {"subject": "shelf.11", "predicate": "on", "object": "table.15"}, {"subject": "shelf.9", "predicate": "on", "object": "table.15"}, {"subject": "lamp.10", "predicate": "on", "object": "table.2"}, {"subject": "door.4", "predicate": "on", "object": "table.2"}, {"subject": "door.6", "predicate": "on", "object": "table.2"}, {"subject": "flower.5", "predicate": "on", "object": "table.15"}]
90
Generate a structured scene graph for an image of size (800 x 534) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 534) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [360, 327, 640, 475]}, {"id": "shelf.2", "bbox": [358, 369, 638, 468]}, {"id": "stand.3", "bbox": [622, 329, 756, 414]}, {"id": "table.4", "bbox": [211, 216, 346, 267]}, {"id": "cabinet.5", "bbox": [143, 199, 232, 271]}, {"id": "window.6", "bbox": [140, 110, 211, 184]}, {"id": "light.7", "bbox": [254, 50, 306, 167]}, {"id": "chair.8", "bbox": [282, 212, 335, 264]}, {"id": "cabinet.9", "bbox": [202, 112, 267, 153]}, {"id": "sink.10", "bbox": [145, 179, 209, 214]}, {"id": "window.11", "bbox": [129, 135, 202, 187]}, {"id": "sink.12", "bbox": [143, 194, 212, 213]}]
[{"subject": "cabinet.5", "predicate": "under", "object": "sink.10"}, {"subject": "chair.8", "predicate": "at", "object": "table.4"}, {"subject": "window.6", "predicate": "above", "object": "sink.12"}]
93
Generate a structured scene graph for an image of size (335 x 500) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (335 x 500) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 103, 333, 487]}, {"id": "plate.2", "bbox": [48, 279, 251, 403]}, {"id": "glass.3", "bbox": [44, 153, 91, 281]}, {"id": "glass.4", "bbox": [194, 144, 228, 271]}, {"id": "glass.5", "bbox": [19, 204, 61, 294]}, {"id": "glass.6", "bbox": [0, 128, 32, 243]}, {"id": "glass.7", "bbox": [239, 197, 281, 285]}, {"id": "glass.8", "bbox": [293, 110, 318, 176]}, {"id": "glass.9", "bbox": [185, 68, 207, 154]}, {"id": "glass.10", "bbox": [100, 72, 120, 140]}, {"id": "glass.11", "bbox": [298, 174, 333, 234]}, {"id": "glass.12", "bbox": [6, 89, 32, 141]}, {"id": "glass.13", "bbox": [310, 109, 333, 183]}, {"id": "glass.14", "bbox": [270, 80, 294, 175]}, {"id": "glass.15", "bbox": [25, 129, 54, 176]}, {"id": "glass.16", "bbox": [204, 92, 229, 139]}, {"id": "glass.17", "bbox": [121, 109, 145, 153]}, {"id": "glass.18", "bbox": [217, 112, 244, 157]}]
[{"subject": "glass.7", "predicate": "on", "object": "table.1"}, {"subject": "glass.4", "predicate": "on", "object": "table.1"}, {"subject": "glass.10", "predicate": "on", "object": "table.1"}, {"subject": "glass.16", "predicate": "on", "object": "table.1"}, {"subject": "glass.18", "predicate": "on", "object": "table.1"}, {"subject": "glass.14", "predicate": "on", "object": "table.1"}, {"subject": "glass.17", "predicate": "on", "object": "table.1"}, {"subject": "glass.8", "predicate": "on", "object": "table.1"}, {"subject": "glass.13", "predicate": "on", "object": "table.1"}, {"subject": "glass.15", "predicate": "on", "object": "table.1"}, {"subject": "glass.12", "predicate": "on", "object": "table.1"}, {"subject": "glass.9", "predicate": "on", "object": "table.1"}, {"subject": "glass.11", "predicate": "on", "object": "table.1"}, {"subject": "glass.6", "predicate": "on", "object": "table.1"}, {"subject": "glass.3", "predicate": "on", "object": "table.1"}, {"subject": "glass.17", "predicate": "on", "object": "table.1"}, {"subject": "glass.5", "predicate": "on", "object": "table.1"}, {"subject": "glass.8", "predicate": "on", "object": "table.1"}, {"subject": "glass.15", "predicate": "on", "object": "table.1"}, {"subject": "glass.7", "predicate": "on", "object": "table.1"}, {"subject": "plate.2", "predicate": "on", "object": "table.1"}, {"subject": "glass.17", "predicate": "on", "object": "table.1"}, {"subject": "glass.10", "predicate": "on", "object": "table.1"}, {"subject": "glass.7", "predicate": "on", "object": "table.1"}]
94
Generate a structured scene graph for an image of size (500 x 375) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (500 x 375) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 0, 444, 374]}, {"id": "plate.2", "bbox": [76, 16, 434, 363]}, {"id": "glass.3", "bbox": [392, 0, 498, 145]}, {"id": "fruit.4", "bbox": [151, 73, 269, 157]}, {"id": "tail.5", "bbox": [311, 117, 343, 143]}, {"id": "orange.6", "bbox": [200, 95, 262, 142]}]
[{"subject": "fruit.4", "predicate": "on", "object": "plate.2"}, {"subject": "plate.2", "predicate": "on", "object": "table.1"}, {"subject": "orange.6", "predicate": "on", "object": "plate.2"}]
95
Generate a structured scene graph for an image of size (359 x 500) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (359 x 500) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 0, 358, 498]}, {"id": "plate.2", "bbox": [51, 209, 347, 489]}, {"id": "glass.3", "bbox": [102, 15, 193, 189]}, {"id": "glass.4", "bbox": [174, 25, 261, 176]}, {"id": "glass.5", "bbox": [260, 93, 343, 211]}, {"id": "glass.6", "bbox": [42, 34, 104, 191]}, {"id": "fork.7", "bbox": [88, 187, 237, 226]}, {"id": "building.8", "bbox": [239, 85, 279, 129]}]
[{"subject": "glass.5", "predicate": "on", "object": "table.1"}, {"subject": "glass.3", "predicate": "on", "object": "table.1"}, {"subject": "glass.6", "predicate": "on", "object": "table.1"}]
96
Generate a structured scene graph for an image of size (375 x 500) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (375 x 500) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 323, 374, 496]}, {"id": "chair.2", "bbox": [57, 183, 212, 348]}, {"id": "plate.3", "bbox": [41, 395, 210, 471]}, {"id": "cup.4", "bbox": [135, 338, 174, 389]}, {"id": "fork.5", "bbox": [220, 401, 284, 454]}, {"id": "handle.6", "bbox": [214, 352, 236, 383]}]
[{"subject": "chair.2", "predicate": "near", "object": "table.1"}]
97
Generate a structured scene graph for an image of size (448 x 336) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (448 x 336) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 0, 447, 335]}, {"id": "bag.2", "bbox": [61, 11, 235, 198]}, {"id": "bag.3", "bbox": [143, 0, 211, 117]}, {"id": "handle.4", "bbox": [379, 267, 400, 330]}]
[{"subject": "bag.3", "predicate": "on", "object": "table.1"}]
98
Generate a structured scene graph for an image of size (500 x 375) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (500 x 375) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "plate.1", "bbox": [0, 163, 230, 320]}, {"id": "cup.2", "bbox": [98, 25, 162, 145]}, {"id": "fork.3", "bbox": [205, 168, 279, 301]}, {"id": "table.4", "bbox": [0, 0, 499, 373]}, {"id": "glass.5", "bbox": [139, 0, 180, 70]}]
[{"subject": "fork.3", "predicate": "near", "object": "plate.1"}]
101
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [380, 0, 797, 362]}, {"id": "street.2", "bbox": [125, 412, 679, 551]}, {"id": "tree.3", "bbox": [105, 262, 164, 360]}, {"id": "tree.4", "bbox": [29, 258, 103, 353]}, {"id": "tree.5", "bbox": [0, 229, 97, 359]}, {"id": "tree.6", "bbox": [700, 0, 799, 360]}, {"id": "car.7", "bbox": [149, 308, 462, 457]}, {"id": "tree.8", "bbox": [361, 182, 406, 331]}, {"id": "car.9", "bbox": [0, 342, 105, 439]}, {"id": "branch.10", "bbox": [571, 117, 646, 291]}, {"id": "leaf.11", "bbox": [0, 0, 86, 49]}, {"id": "trunk.12", "bbox": [586, 278, 621, 363]}, {"id": "leaf.13", "bbox": [115, 124, 236, 206]}, {"id": "leaf.14", "bbox": [300, 232, 344, 281]}, {"id": "leaf.15", "bbox": [219, 247, 282, 322]}, {"id": "leaf.16", "bbox": [86, 158, 157, 217]}, {"id": "trunk.17", "bbox": [183, 293, 211, 360]}, {"id": "boat.18", "bbox": [262, 310, 394, 335]}, {"id": "sign.19", "bbox": [489, 285, 514, 353]}, {"id": "windshield.20", "bbox": [36, 351, 93, 375]}, {"id": "leaf.21", "bbox": [104, 219, 146, 253]}, {"id": "leaf.22", "bbox": [288, 208, 394, 258]}, {"id": "tree.23", "bbox": [241, 160, 450, 270]}, {"id": "tree.24", "bbox": [197, 169, 432, 297]}, {"id": "leaf.25", "bbox": [72, 259, 107, 325]}, {"id": "leaf.26", "bbox": [18, 223, 75, 278]}]
[{"subject": "leaf.22", "predicate": "on", "object": "tree.23"}, {"subject": "leaf.14", "predicate": "on", "object": "tree.8"}, {"subject": "leaf.11", "predicate": "on", "object": "tree.1"}, {"subject": "leaf.15", "predicate": "on", "object": "tree.1"}, {"subject": "leaf.21", "predicate": "on", "object": "tree.3"}, {"subject": "leaf.25", "predicate": "on", "object": "tree.3"}, {"subject": "leaf.26", "predicate": "on", "object": "tree.4"}, {"subject": "leaf.13", "predicate": "on", "object": "tree.6"}, {"subject": "tree.5", "predicate": "has", "object": "leaf.16"}]
102
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 800, 451]}, {"id": "car.2", "bbox": [229, 347, 735, 534]}, {"id": "street.3", "bbox": [0, 487, 798, 594]}, {"id": "window.4", "bbox": [532, 171, 615, 324]}, {"id": "door.5", "bbox": [355, 211, 433, 353]}, {"id": "window.6", "bbox": [722, 182, 793, 312]}, {"id": "window.7", "bbox": [197, 193, 263, 318]}, {"id": "tire.8", "bbox": [278, 462, 363, 539]}, {"id": "tire.9", "bbox": [583, 464, 657, 534]}, {"id": "window.10", "bbox": [718, 0, 784, 71]}, {"id": "window.11", "bbox": [30, 30, 87, 97]}, {"id": "window.12", "bbox": [208, 22, 261, 72]}, {"id": "window.13", "bbox": [411, 378, 507, 433]}, {"id": "wheel.14", "bbox": [293, 469, 353, 529]}]
[{"subject": "window.6", "predicate": "on", "object": "building.1"}, {"subject": "window.4", "predicate": "on", "object": "building.1"}, {"subject": "window.7", "predicate": "on", "object": "building.1"}, {"subject": "door.5", "predicate": "on", "object": "building.1"}, {"subject": "window.10", "predicate": "on", "object": "building.1"}, {"subject": "wheel.14", "predicate": "on", "object": "car.2"}, {"subject": "wheel.14", "predicate": "on", "object": "car.2"}, {"subject": "window.13", "predicate": "on", "object": "car.2"}, {"subject": "window.11", "predicate": "on", "object": "car.2"}, {"subject": "tire.8", "predicate": "on", "object": "car.2"}, {"subject": "tire.8", "predicate": "on", "object": "car.2"}, {"subject": "window.13", "predicate": "on", "object": "car.2"}]
103
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "tree.1", "bbox": [0, 0, 391, 471]}, {"id": "window.2", "bbox": [232, 278, 287, 339]}, {"id": "building.3", "bbox": [94, 176, 440, 420]}, {"id": "building.4", "bbox": [0, 100, 111, 436]}, {"id": "tree.5", "bbox": [621, 196, 771, 409]}, {"id": "car.6", "bbox": [688, 364, 794, 539]}, {"id": "sidewalk.7", "bbox": [292, 402, 483, 469]}, {"id": "window.8", "bbox": [318, 176, 363, 232]}, {"id": "window.9", "bbox": [311, 280, 355, 340]}, {"id": "window.10", "bbox": [368, 294, 403, 349]}, {"id": "window.11", "bbox": [165, 285, 192, 340]}, {"id": "car.12", "bbox": [515, 367, 620, 437]}, {"id": "vehicle.13", "bbox": [517, 365, 620, 435]}]
[{"subject": "window.11", "predicate": "on", "object": "building.3"}, {"subject": "window.9", "predicate": "on", "object": "building.3"}, {"subject": "window.8", "predicate": "on", "object": "building.3"}]
104
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "car.1", "bbox": [0, 88, 798, 502]}, {"id": "light.2", "bbox": [0, 1, 668, 81]}, {"id": "window.3", "bbox": [150, 160, 448, 262]}, {"id": "light.4", "bbox": [65, 284, 324, 364]}, {"id": "tire.5", "bbox": [364, 360, 471, 501]}, {"id": "box.6", "bbox": [42, 54, 203, 128]}, {"id": "window.7", "bbox": [491, 169, 617, 246]}, {"id": "window.8", "bbox": [44, 132, 167, 209]}, {"id": "tire.9", "bbox": [2, 257, 67, 374]}, {"id": "window.10", "bbox": [143, 130, 260, 199]}, {"id": "window.11", "bbox": [433, 173, 522, 260]}, {"id": "roof.12", "bbox": [0, 125, 199, 143]}, {"id": "car.13", "bbox": [739, 111, 798, 173]}, {"id": "door.14", "bbox": [651, 82, 688, 163]}, {"id": "tire.15", "bbox": [650, 267, 695, 355]}, {"id": "plate.16", "bbox": [102, 378, 155, 433]}, {"id": "building.17", "bbox": [265, 36, 315, 95]}, {"id": "window.18", "bbox": [233, 96, 308, 130]}, {"id": "window.19", "bbox": [307, 100, 348, 138]}, {"id": "window.20", "bbox": [350, 96, 389, 131]}, {"id": "window.21", "bbox": [384, 96, 421, 131]}]
[{"subject": "car.1", "predicate": "has", "object": "window.10"}, {"subject": "car.1", "predicate": "has", "object": "window.8"}, {"subject": "car.1", "predicate": "has", "object": "tire.9"}, {"subject": "car.1", "predicate": "has", "object": "window.8"}, {"subject": "car.1", "predicate": "has", "object": "window.10"}, {"subject": "car.1", "predicate": "has", "object": "roof.12"}]
105
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 216, 798, 596]}, {"id": "building.2", "bbox": [660, 0, 794, 600]}, {"id": "man.3", "bbox": [539, 146, 660, 462]}, {"id": "car.4", "bbox": [325, 160, 522, 317]}, {"id": "coat.5", "bbox": [539, 173, 660, 315]}, {"id": "car.6", "bbox": [0, 89, 66, 336]}, {"id": "jean.7", "bbox": [572, 308, 636, 449]}, {"id": "truck.8", "bbox": [365, 97, 500, 164]}, {"id": "car.9", "bbox": [0, 155, 92, 253]}, {"id": "vehicle.10", "bbox": [46, 155, 128, 244]}, {"id": "vehicle.11", "bbox": [101, 145, 192, 234]}, {"id": "pole.12", "bbox": [498, 0, 515, 198]}, {"id": "car.13", "bbox": [180, 160, 257, 224]}, {"id": "car.14", "bbox": [224, 157, 290, 219]}, {"id": "vehicle.15", "bbox": [264, 154, 311, 211]}, {"id": "car.16", "bbox": [116, 151, 194, 232]}, {"id": "vehicle.17", "bbox": [317, 157, 510, 307]}, {"id": "vehicle.18", "bbox": [7, 152, 95, 250]}]
[{"subject": "car.4", "predicate": "on", "object": "street.1"}, {"subject": "car.16", "predicate": "on", "object": "street.1"}, {"subject": "car.13", "predicate": "on", "object": "street.1"}, {"subject": "car.9", "predicate": "on", "object": "street.1"}, {"subject": "car.14", "predicate": "on", "object": "street.1"}, {"subject": "truck.8", "predicate": "on", "object": "street.1"}, {"subject": "car.6", "predicate": "on", "object": "street.1"}, {"subject": "vehicle.15", "predicate": "on", "object": "street.1"}, {"subject": "car.13", "predicate": "on", "object": "street.1"}, {"subject": "car.4", "predicate": "on", "object": "street.1"}, {"subject": "truck.8", "predicate": "parked on", "object": "street.1"}, {"subject": "vehicle.17", "predicate": "parked on", "object": "street.1"}, {"subject": "car.13", "predicate": "above", "object": "street.1"}, {"subject": "car.16", "predicate": "above", "object": "street.1"}, {"subject": "car.9", "predicate": "above", "object": "street.1"}, {"subject": "car.4", "predicate": "above", "object": "street.1"}, {"subject": "car.14", "predicate": "above", "object": "street.1"}, {"subject": "vehicle.18", "predicate": "parked on", "object": "street.1"}, {"subject": "vehicle.10", "predicate": "parked on", "object": "street.1"}, {"subject": "vehicle.11", "predicate": "parked on", "object": "street.1"}, {"subject": "car.13", "predicate": "parked on", "object": "street.1"}, {"subject": "vehicle.15", "predicate": "parked on", "object": "street.1"}, {"subject": "man.3", "predicate": "walking on", "object": "street.1"}, {"subject": "man.3", "predicate": "has", "object": "coat.5"}, {"subject": "car.16", "predicate": "above", "object": "street.1"}, {"subject": "car.9", "predicate": "above", "object": "street.1"}, {"subject": "car.4", "predicate": "above", "object": "street.1"}, {"subject": "car.14", "predicate": "above", "object": "street.1"}, {"subject": "car.13", "predicate": "above", "object": "street.1"}, {"subject": "building.2", "predicate": "near", "object": "street.1"}, {"subject": "man.3", "predicate": "wearing", "object": "coat.5"}, {"subject": "pole.12", "predicate": "near", "object": "truck.8"}, {"subject": "car.4", "predicate": "above", "object": "street.1"}, {"subject": "car.14", "predicate": "above", "object": "street.1"}, {"subject": "car.13", "predicate": "above", "object": "street.1"}, {"subject": "car.16", "predicate": "above", "object": "street.1"}, {"subject": "car.9", "predicate": "above", "object": "street.1"}]
106
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [75, 8, 792, 356]}, {"id": "window.2", "bbox": [407, 28, 490, 160]}, {"id": "man.3", "bbox": [450, 289, 569, 478]}, {"id": "truck.4", "bbox": [603, 162, 797, 374]}, {"id": "tree.5", "bbox": [328, 4, 798, 317]}, {"id": "branch.6", "bbox": [314, 17, 792, 147]}, {"id": "sign.7", "bbox": [672, 457, 749, 592]}, {"id": "man.8", "bbox": [221, 303, 300, 521]}, {"id": "sidewalk.9", "bbox": [56, 353, 382, 478]}]
[{"subject": "building.1", "predicate": "has", "object": "window.2"}, {"subject": "tree.5", "predicate": "has", "object": "branch.6"}, {"subject": "window.2", "predicate": "on", "object": "building.1"}, {"subject": "man.8", "predicate": "on", "object": "sidewalk.9"}, {"subject": "man.8", "predicate": "on", "object": "sidewalk.9"}]
107
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [7, 214, 794, 596]}, {"id": "chair.2", "bbox": [394, 306, 724, 596]}, {"id": "box.3", "bbox": [0, 211, 162, 316]}, {"id": "paper.4", "bbox": [478, 237, 672, 296]}, {"id": "chair.5", "bbox": [387, 46, 444, 165]}, {"id": "box.6", "bbox": [310, 415, 402, 461]}, {"id": "leg.7", "bbox": [428, 547, 519, 596]}, {"id": "book.8", "bbox": [750, 253, 798, 303]}]
[{"subject": "box.6", "predicate": "under", "object": "table.1"}, {"subject": "chair.2", "predicate": "has", "object": "leg.7"}, {"subject": "chair.2", "predicate": "has", "object": "leg.7"}, {"subject": "paper.4", "predicate": "on", "object": "table.1"}, {"subject": "book.8", "predicate": "on", "object": "table.1"}]
108
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 326, 796, 596]}, {"id": "tree.2", "bbox": [141, 15, 533, 342]}, {"id": "tree.3", "bbox": [505, 33, 675, 315]}, {"id": "building.4", "bbox": [0, 0, 106, 342]}, {"id": "tree.5", "bbox": [660, 17, 798, 341]}, {"id": "building.6", "bbox": [613, 0, 797, 98]}, {"id": "street.7", "bbox": [0, 322, 228, 395]}, {"id": "car.8", "bbox": [607, 321, 774, 382]}, {"id": "window.9", "bbox": [29, 151, 48, 210]}, {"id": "pole.10", "bbox": [355, 0, 378, 355]}, {"id": "truck.11", "bbox": [485, 285, 548, 353]}, {"id": "sidewalk.12", "bbox": [0, 333, 135, 350]}, {"id": "door.13", "bbox": [685, 321, 754, 371]}, {"id": "car.14", "bbox": [560, 315, 612, 354]}, {"id": "person.15", "bbox": [35, 298, 63, 346]}, {"id": "person.16", "bbox": [155, 300, 182, 350]}, {"id": "bike.17", "bbox": [319, 317, 353, 356]}, {"id": "people.18", "bbox": [158, 303, 186, 352]}, {"id": "tree.19", "bbox": [389, 56, 552, 262]}]
[{"subject": "door.13", "predicate": "on", "object": "car.8"}, {"subject": "door.13", "predicate": "on", "object": "car.8"}, {"subject": "tree.2", "predicate": "on", "object": "street.1"}, {"subject": "person.15", "predicate": "on", "object": "sidewalk.12"}, {"subject": "car.14", "predicate": "on", "object": "street.1"}, {"subject": "car.8", "predicate": "with", "object": "door.13"}, {"subject": "tree.5", "predicate": "behind", "object": "building.6"}, {"subject": "tree.19", "predicate": "behind", "object": "building.6"}, {"subject": "tree.3", "predicate": "behind", "object": "building.6"}, {"subject": "person.15", "predicate": "across", "object": "street.1"}, {"subject": "building.4", "predicate": "on", "object": "street.1"}, {"subject": "car.14", "predicate": "near", "object": "truck.11"}, {"subject": "building.4", "predicate": "with", "object": "window.9"}]
109
Generate a structured scene graph for an image of size (640 x 480) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (640 x 480) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "building.1", "bbox": [0, 0, 229, 301]}, {"id": "street.2", "bbox": [0, 275, 639, 478]}, {"id": "building.3", "bbox": [226, 0, 401, 288]}, {"id": "building.4", "bbox": [401, 0, 498, 275]}, {"id": "building.5", "bbox": [494, 0, 637, 295]}, {"id": "car.6", "bbox": [368, 257, 543, 320]}, {"id": "man.7", "bbox": [186, 230, 256, 337]}, {"id": "window.8", "bbox": [132, 77, 160, 135]}, {"id": "window.9", "bbox": [66, 70, 96, 136]}, {"id": "window.10", "bbox": [178, 78, 202, 137]}, {"id": "window.11", "bbox": [245, 20, 268, 73]}, {"id": "building.12", "bbox": [121, 0, 249, 252]}]
[{"subject": "building.1", "predicate": "on", "object": "street.2"}, {"subject": "window.9", "predicate": "of", "object": "building.3"}, {"subject": "window.8", "predicate": "of", "object": "building.4"}, {"subject": "window.10", "predicate": "of", "object": "building.12"}, {"subject": "window.11", "predicate": "of", "object": "building.5"}]
110
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "room.1", "bbox": [0, 0, 799, 598]}, {"id": "table.2", "bbox": [62, 273, 582, 592]}, {"id": "window.3", "bbox": [316, 18, 728, 239]}, {"id": "chair.4", "bbox": [500, 292, 700, 586]}, {"id": "chair.5", "bbox": [550, 407, 759, 600]}, {"id": "curtain.6", "bbox": [586, 33, 721, 235]}, {"id": "curtain.7", "bbox": [319, 41, 435, 214]}, {"id": "door.8", "bbox": [0, 0, 80, 331]}, {"id": "chair.9", "bbox": [151, 264, 301, 410]}, {"id": "chair.10", "bbox": [567, 225, 721, 367]}, {"id": "chair.11", "bbox": [278, 232, 375, 339]}, {"id": "chair.12", "bbox": [450, 228, 545, 283]}, {"id": "arm.13", "bbox": [592, 439, 744, 469]}]
[{"subject": "curtain.7", "predicate": "on", "object": "window.3"}, {"subject": "curtain.6", "predicate": "on", "object": "window.3"}, {"subject": "chair.12", "predicate": "on", "object": "table.2"}]
111
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "handle.1", "bbox": [654, 122, 792, 153]}, {"id": "paper.2", "bbox": [543, 10, 796, 145]}, {"id": "counter.3", "bbox": [15, 7, 784, 480]}, {"id": "handle.4", "bbox": [32, 0, 117, 112]}, {"id": "bottle.5", "bbox": [326, 5, 464, 271]}, {"id": "counter.6", "bbox": [437, 0, 782, 428]}]
[{"subject": "paper.2", "predicate": "on", "object": "counter.3"}]
112
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "table.1", "bbox": [0, 246, 780, 385]}, {"id": "chair.2", "bbox": [537, 327, 793, 594]}, {"id": "chair.3", "bbox": [61, 355, 320, 597]}, {"id": "screen.4", "bbox": [574, 143, 707, 242]}, {"id": "screen.5", "bbox": [312, 146, 444, 245]}, {"id": "screen.6", "bbox": [75, 148, 198, 242]}, {"id": "paper.7", "bbox": [354, 134, 432, 225]}, {"id": "leg.8", "bbox": [216, 535, 305, 584]}, {"id": "leg.9", "bbox": [674, 528, 784, 561]}, {"id": "leg.10", "bbox": [158, 532, 217, 596]}, {"id": "leg.11", "bbox": [544, 524, 650, 554]}, {"id": "leg.12", "bbox": [106, 534, 196, 560]}, {"id": "leg.13", "bbox": [655, 534, 683, 596]}, {"id": "seat.14", "bbox": [550, 388, 605, 461]}]
[{"subject": "seat.14", "predicate": "of", "object": "chair.2"}]
113
Generate a structured scene graph for an image of size (800 x 600) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (800 x 600) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "street.1", "bbox": [0, 350, 799, 596]}, {"id": "building.2", "bbox": [288, 0, 794, 369]}, {"id": "building.3", "bbox": [0, 0, 298, 343]}, {"id": "bike.4", "bbox": [528, 493, 797, 596]}, {"id": "pole.5", "bbox": [660, 0, 707, 600]}, {"id": "tree.6", "bbox": [0, 163, 98, 327]}, {"id": "car.7", "bbox": [352, 309, 503, 379]}, {"id": "people.8", "bbox": [583, 325, 606, 390]}, {"id": "door.9", "bbox": [462, 269, 500, 343]}, {"id": "pole.10", "bbox": [146, 257, 170, 382]}, {"id": "pole.11", "bbox": [408, 498, 429, 596]}, {"id": "window.12", "bbox": [744, 71, 781, 123]}, {"id": "person.13", "bbox": [169, 307, 206, 403]}, {"id": "window.14", "bbox": [525, 190, 573, 256]}, {"id": "window.15", "bbox": [465, 10, 496, 79]}, {"id": "window.16", "bbox": [414, 16, 444, 85]}, {"id": "person.17", "bbox": [210, 314, 240, 408]}, {"id": "window.18", "bbox": [364, 107, 391, 174]}, {"id": "window.19", "bbox": [464, 204, 502, 254]}, {"id": "person.20", "bbox": [532, 320, 554, 385]}, {"id": "people.21", "bbox": [528, 312, 610, 389]}, {"id": "railing.22", "bbox": [300, 130, 794, 180]}]
[{"subject": "building.2", "predicate": "with", "object": "window.15"}, {"subject": "building.2", "predicate": "with", "object": "window.16"}, {"subject": "building.2", "predicate": "with", "object": "window.12"}, {"subject": "building.2", "predicate": "with", "object": "window.14"}, {"subject": "building.2", "predicate": "with", "object": "window.18"}, {"subject": "railing.22", "predicate": "near", "object": "building.2"}, {"subject": "person.20", "predicate": "on", "object": "street.1"}, {"subject": "bike.4", "predicate": "on", "object": "pole.5"}, {"subject": "door.9", "predicate": "has", "object": "window.19"}, {"subject": "tree.6", "predicate": "in front of", "object": "building.3"}]
114
Generate a structured scene graph for an image of size (256 x 256) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (256 x 256) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "man.1", "bbox": [54, 8, 254, 255]}, {"id": "woman.2", "bbox": [1, 42, 117, 255]}, {"id": "shirt.3", "bbox": [107, 50, 255, 208]}, {"id": "shirt.4", "bbox": [8, 100, 93, 189]}, {"id": "jean.5", "bbox": [24, 185, 117, 255]}, {"id": "jean.6", "bbox": [164, 185, 240, 255]}, {"id": "hair.7", "bbox": [30, 41, 79, 103]}, {"id": "hair.8", "bbox": [147, 9, 194, 54]}, {"id": "bag.9", "bbox": [0, 181, 31, 256]}, {"id": "arm.10", "bbox": [80, 103, 102, 185]}, {"id": "hand.11", "bbox": [37, 173, 67, 209]}, {"id": "leaf.12", "bbox": [118, 0, 137, 12]}, {"id": "ear.13", "bbox": [171, 37, 182, 52]}]
[{"subject": "man.1", "predicate": "wearing", "object": "jean.6"}, {"subject": "woman.2", "predicate": "wearing", "object": "jean.5"}, {"subject": "man.1", "predicate": "wearing", "object": "shirt.3"}, {"subject": "woman.2", "predicate": "has", "object": "arm.10"}, {"subject": "man.1", "predicate": "has", "object": "ear.13"}, {"subject": "woman.2", "predicate": "has", "object": "hair.7"}, {"subject": "woman.2", "predicate": "has", "object": "bag.9"}, {"subject": "woman.2", "predicate": "wearing", "object": "jean.5"}, {"subject": "woman.2", "predicate": "has", "object": "hand.11"}, {"subject": "woman.2", "predicate": "has", "object": "hair.7"}, {"subject": "woman.2", "predicate": "has", "object": "bag.9"}, {"subject": "woman.2", "predicate": "has", "object": "hand.11"}, {"subject": "man.1", "predicate": "has", "object": "hair.8"}, {"subject": "woman.2", "predicate": "wearing", "object": "shirt.4"}, {"subject": "man.1", "predicate": "wearing", "object": "shirt.3"}, {"subject": "man.1", "predicate": "has", "object": "ear.13"}, {"subject": "man.1", "predicate": "wearing", "object": "jean.6"}, {"subject": "woman.2", "predicate": "wearing", "object": "jean.5"}, {"subject": "woman.2", "predicate": "has", "object": "hair.7"}, {"subject": "man.1", "predicate": "wearing", "object": "shirt.3"}, {"subject": "man.1", "predicate": "has", "object": "hair.8"}]
115
Generate a structured scene graph for an image of size (256 x 256) using the following format: <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign a unique ID for each object using the format `"object_name.number"` (e.g., `"person.1"`, `"bike.2"`). - Provide its bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Represent interactions accurately using `"subject"`, `"predicate"`, and `"object"`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
Generate a structured scene graph for an image of size (256 x 256) using the specified object and relationship categories. ### **Output Format:** <answer> { "objects": [ {"id": "object_name.number", "bbox": [x1, y1, x2, y2]}, ... ], "relationships": [ {"subject": "object_name.number", "predicate": "relationship_type", "object": "object_name.number"}, ... ] } </answer> ### **Guidelines:** - **Objects:** - Assign unique IDs in the format `"object_name.number"` (e.g., `"person.1"`). The **object_name** must belong to the predefined object set: `["airplane", "animal", "arm", "bag", "banana", "basket", "beach", "bear", "bed", "bench", "bike", "bird", "board", "boat", "book", "boot", "bottle", "bowl", "box", "boy", "branch", "building", "bus", "cabinet", "cap", "car", "cat", "chair", "child", "clock", "coat", "counter", "cow", "cup", "curtain", "desk", "dog", "door", "drawer", "ear", "elephant", "engine", "eye", "face", "fence", "finger", "flag", "flower", "food", "fork", "fruit", "giraffe", "girl", "glass", "glove", "guy", "hair", "hand", "handle", "hat", "head", "helmet", "hill", "horse", "house", "jacket", "jean", "kid", "kite", "lady", "lamp", "laptop", "leaf", "leg", "letter", "light", "logo", "man", "men", "motorcycle", "mountain", "mouth", "neck", "nose", "number", "orange", "pant", "paper", "paw", "people", "person", "phone", "pillow", "pizza", "plane", "plant", "plate", "player", "pole", "post", "pot", "racket", "railing", "rock", "roof", "room", "screen", "seat", "sheep", "shelf", "shirt", "shoe", "short", "sidewalk", "sign", "sink", "skateboard", "ski", "skier", "sneaker", "snow", "sock", "stand", "street", "surfboard", "table", "tail", "tie", "tile", "tire", "toilet", "towel", "tower", "track", "train", "tree", "truck", "trunk", "umbrella", "vase", "vegetable", "vehicle", "wave", "wheel", "window", "windshield", "wing", "wire", "woman", "zebra"]`. - Provide a bounding box `[x1, y1, x2, y2]` in integer pixel format. - Include all visible objects, even if they have no relationships. - **Relationships:** - Define relationships using `"subject"`, `"predicate"`, and `"object"`. - The **predicate** must belong to the predefined relationship set: `["above", "across", "against", "along", "and", "at", "attached to", "behind", "belonging to", "between", "carrying", "covered in", "covering", "eating", "flying in", "for", "from", "growing on", "hanging from", "has", "holding", "in", "in front of", "laying on", "looking at", "lying on", "made of", "mounted on", "near", "of", "on", "on back of", "over", "painted on", "parked on", "part of", "playing", "riding", "says", "sitting on", "standing on", "to", "under", "using", "walking in", "walking on", "watching", "wearing", "wears", "with"]`. - Omit relationships for orphan objects. ### **Example Output:** <answer> { "objects": [ {"id": "person.1", "bbox": [120, 200, 350, 700]}, {"id": "bike.2", "bbox": [100, 600, 400, 800]}, {"id": "helmet.3", "bbox": [150, 150, 280, 240]}, {"id": "tree.4", "bbox": [500, 100, 750, 700]} ], "relationships": [ {"subject": "person.1", "predicate": "riding", "object": "bike.2"}, {"subject": "person.1", "predicate": "wearing", "object": "helmet.3"} ] } </answer> Now, generate the complete scene graph for the provided image:
[{"id": "people.1", "bbox": [0, 15, 208, 224]}, {"id": "man.2", "bbox": [0, 26, 119, 223]}, {"id": "pant.3", "bbox": [0, 129, 119, 225]}, {"id": "woman.4", "bbox": [133, 14, 206, 170]}, {"id": "table.5", "bbox": [20, 171, 190, 202]}, {"id": "shirt.6", "bbox": [2, 72, 100, 151]}, {"id": "tie.7", "bbox": [35, 78, 54, 135]}, {"id": "hand.8", "bbox": [41, 131, 71, 157]}]
[{"subject": "man.2", "predicate": "has", "object": "tie.7"}, {"subject": "man.2", "predicate": "wearing", "object": "shirt.6"}, {"subject": "man.2", "predicate": "wearing", "object": "pant.3"}, {"subject": "man.2", "predicate": "wearing", "object": "tie.7"}, {"subject": "man.2", "predicate": "wearing", "object": "shirt.6"}]