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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"}]
27
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"}]
28
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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This dataset, derived from VG150, provides image-text pairs for scene graph generation. Each example includes an image, an "open" prompt, a "close" prompt, a list of objects, and their relationships. It's designed to be used for training and evaluating models that generate scene graphs from images and textual prompts.

This dataset is used in the paper R1-SGG: Compile Scene Graphs with Reinforcement Learning.

The dataset is structured as follows:

  • image_id: Unique identifier for the image.
  • image: The image itself.
  • prompt_open: An open-ended prompt related to the image.
  • prompt_close: A more specific prompt related to the image.
  • objects: A list of objects present in the image.
  • relationships: A description of the relationships between the objects.

Data Usage:

The dataset can be loaded using the datasets library:

from datasets import load_dataset

db_train = load_dataset("JosephZ/vg150_train_sgg_prompt")["train"]
db_val = load_dataset("JosephZ/vg150_val_sgg_prompt")["train"]

(Further instructions from the original README regarding training and inference can be included here)

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