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CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement | Noranart Vesdapunt, Baoyuan Wang | Face detection is a fundamental problem for many downstream face applications, and there is a rising demand for faster, more accurate yet support for higher resolution face detectors. Recent smartphones can record a video in 8K resolution, but many of the existing face detectors still fail due to the anchor size and training data. We analyze the failure cases and observe a large number of correct predicted boxes with incorrect confidences. To calibrate these confidences, we propose a confidence ranking network with a pairwise ranking loss to re-rank the predicted confidences locally within the same image. Our confidence ranker is model-agnostic, so we can augment the data by choosing the pairs from multiple face detectors during the training, and generalize to a wide range of face detectors during the testing. On WiderFace, we achieve the highest AP on the single-scale, and our AP is competitive with the previous multi-scale methods while being significantly faster. On 8K resolution, our method solves the GPU memory issue and allows us to indirectly train on 8K. We collect 8K resolution test set to show the improvement, and we will release our test set as a new benchmark for future research. | https://openaccess.thecvf.com/content/CVPR2021/papers/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07017 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vesdapunt_CRFace_Confidence_Ranker_CVPR_2021_supplemental.pdf | null |
Semantic Audio-Visual Navigation | Changan Chen, Ziad Al-Halah, Kristen Grauman | Recent work on audio-visual navigation assumes a constantly-sounding target and restricts the role of audio to signaling the target's position. We introduce semantic audio-visual navigation, where objects in the environment make sounds consistent with their semantic meaning (e.g., toilet flushing, door creaking) and acoustic events are sporadic or short in duration. We propose a transformer-based model to tackle this new semantic AudioGoal task, incorporating an inferred goal descriptor that captures both spatial and semantic properties of the target. Our model's persistent multimodal memory enables it to reach the goal even long after the acoustic event stops. In support of the new task, we also expand the SoundSpaces audio simulations to provide semantically grounded sounds for an array of objects in Matterport3D. Our method strongly outperforms existing audio-visual navigation methods by learning to associate semantic, acoustic, and visual cues. Project page: http://vision.cs.utexas.edu/projects/semantic-audio-visual-navigation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_supplemental.zip | null |
Humble Teachers Teach Better Students for Semi-Supervised Object Detection | Yihe Tang, Weifeng Chen, Yijun Luo, Yuting Zhang | We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework. Our method is featured with 1) the exponential moving averaging strategy to update the teacher from the student online, 2) using plenty of region proposals and soft pseudo-labels as the student's training targets, and 3) a light-weighted detection-specific data ensemble for the teacher to generate more reliable pseudo labels. Compared to the recent state-of-the-art - STAC, which uses hard labels on sparsely selected hard pseudo samples, the teacher in our model exposes richer information to the student with soft-labels on many proposals. Our model achieves COCO-style AP of 53.04% on VOC07 val set, 8.4% better than STAC, when using VOC12 as unlabeled data. On MS-COCO, it outperforms prior work when only a small percentage of data is taken as labeled. It also reaches 53.8% AP on MS-COCO test-dev with 3.1% gain over the fully supervised ResNet-152 cascaded R-CNN, by tapping into unlabeled data of a similar size to the labeled data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.10456 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Humble_Teachers_Teach_CVPR_2021_supplemental.pdf | null |
One Shot Face Swapping on Megapixels | Yuhao Zhu, Qi Li, Jian Wang, Cheng-Zhong Xu, Zhenan Sun | Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc. Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and representative face swapping images to train DeepFake detection algorithms. This paper proposes the first Megapixel level method for one shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face representation hierarchically by the proposed Hierarchical Representation Face Encoder (HieRFE) in an extended latent space to maintain more facial details, rather than compressed representation in previous face swapping methods. Secondly, a carefully designed Face Transfer Module (FTM) is proposed to transfer the identity from a source image to the target by a non-linear trajectory without explicit feature disentanglement. Finally, the swapped faces can be synthesized by StyleGAN2 with the benefits of its training stability and powerful generative capability. Each part of MegaFS can be trained separately so the requirement of our model for GPU memory can be satisfied for megapixel face swapping. In summary, complete face representation, stable training, and limited memory usage are the three novel contributions to the success of our method. Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_One_Shot_Face_CVPR_2021_supplemental.pdf | null |
CDFI: Compression-Driven Network Design for Frame Interpolation | Tianyu Ding, Luming Liang, Zhihui Zhu, Ilya Zharkov | DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10X compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm. Our source code is available at https://github.com/tding1/CDFI. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10559 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper.html | CVPR 2021 | null | null |
PAConv: Position Adaptive Convolution With Dynamic Kernel Assembling on Point Clouds | Mutian Xu, Runyu Ding, Hengshuang Zhao, Xiaojuan Qi | We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing. The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet. In this way, the kernel is built in a data-driven manner, endowing PAConv with more flexibility than 2D convolutions to better handle the irregular and unordered point cloud data. Besides, the complexity of the learning process is reduced by combining weight matrices instead of brutally predicting kernels from point positions. Furthermore, different from the existing point convolution operators whose network architectures are often heavily engineered, we integrate our PAConv into classical MLP-based point cloud pipelines without changing network configurations. Even built on simple networks, our method still approaches or even surpasses the state-of-the-art models, and significantly improves baseline performance on both classification and segmentation tasks, yet with decent efficiency. Thorough ablation studies and visualizations are provided to understand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14635 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_PAConv_Position_Adaptive_CVPR_2021_supplemental.pdf | null |
End-to-End Object Detection With Fully Convolutional Network | Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng | Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training. In this paper, we give the analysis of discarding NMS, where the results reveal that a proper label assignment plays a crucial role. To this end, for fully convolutional detectors, we introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection, which obtains comparable performance with NMS. Besides, a simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. With these techniques, our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets. The code is available at https://github.com/Megvii-BaseDetection/DeFCN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03544 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_End-to-End_Object_Detection_CVPR_2021_supplemental.pdf | null |
Efficient Initial Pose-Graph Generation for Global SfM | Daniel Barath, Dmytro Mishkin, Ivan Eichhardt, Ilia Shipachev, Jiri Matas | We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric verification by RANSAC, which are the most time-consuming steps of the pose-graph creation, we propose two new methods -- built on the fact that image pairs usually are matched consecutively. Thus, candidate relative poses can be recovered from paths in the partly-built pose-graph. We propose a heuristic for the A* traversal, considering global similarity of images and the quality of the pose-graph edges. Given a relative pose from a path, descriptor-based feature matching is made "light-weight" by exploiting the known epipolar geometry. To speed up PROSAC-based sampling when RANSAC is applied, we propose a third method to order the correspondences by their inlier probabilities from previous estimations. The algorithms are tested on 402130 image pairs from the 1DSfM dataset and they speed up the feature matching 17 times and pose estimation 5 times. The source code will be made public. | https://openaccess.thecvf.com/content/CVPR2021/papers/Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11986 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Barath_Efficient_Initial_Pose-Graph_CVPR_2021_supplemental.pdf | null |
Representative Batch Normalization With Feature Calibration | Shang-Hua Gao, Qi Han, Duo Li, Ming-Ming Cheng, Pai Peng | Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation. The source code is available in http://mmcheng.net/rbn. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper.html | CVPR 2021 | null | null |
VarifocalNet: An IoU-Aware Dense Object Detector | Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sunderhauf | Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by 2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.13367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper.html | CVPR 2021 | null | null |
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation | Youngmin Oh, Beomjun Kim, Bumsub Ham | We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations. Although object bounding boxes are good indicators to segment corresponding objects, they do not specify object boundaries, making it hard to train convolutional neural networks (CNNs) for semantic segmentation. We find that background regions are perceptually consistent in part within an image, and this can be leveraged to discriminate foreground and background regions inside object bounding boxes. To implement this idea, we propose a novel pooling method, dubbed background-aware pooling (BAP), that focuses more on aggregating foreground features inside the bounding boxes using attention maps. This allows to extract high-quality pseudo segmentation labels to train CNNs for semantic segmentation, but the labels still contain noise especially at object boundaries. To address this problem, we also introduce a noise-aware loss (NAL) that makes the networks less susceptible to incorrect labels. Experimental results demonstrate that learning with our pseudo labels already outperforms state-of-the-art weakly- and semi-supervised methods on the PASCAL VOC 2012 dataset, and the NAL further boosts the performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Oh_Background-Aware_Pooling_and_CVPR_2021_supplemental.pdf | null |
Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution | Chi Zhang, Baoxiong Jia, Song-Chun Zhu, Yixin Zhu | Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based on spatial-temporal knowledge in mind, and an applied requirement on a high-level cognitive system capable of navigating and acting in space and time. Recent works have focused on an abstract reasoning task of this kind---Raven's Progressive Matrices (RPM). Despite the encouraging progress on RPM that achieves human-level performance in terms of accuracy, modern approaches have neither a treatment of human-like reasoning on generalization, nor a potential to generate answers. To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner; central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects. Specifically, we disentangle perception and reasoning from a monolithic model. The neural visual perception frontend predicts objects' attributes, later aggregated by a scene inference engine to produce a probabilistic scene representation. In the symbolic logical reasoning backend, the PrAE learner uses the representation to abduce the hidden rules. An answer is predicted by executing the rules on the probabilistic representation. The entire system is trained end-to-end in an analysis-by-synthesis manner without any visual attribute annotations. Extensive experiments demonstrate that the PrAE learner improves cross-configuration generalization and is capable of rendering an answer, in contrast to prior works that merely make a categorical choice from candidates. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Abstract_Spatial-Temporal_Reasoning_CVPR_2021_supplemental.pdf | null |
Reducing Domain Gap by Reducing Style Bias | Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, Donggeun Yoo | Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.pdf | http://arxiv.org/abs/1910.11645 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.html | CVPR 2021 | null | null |
Efficient Regional Memory Network for Video Object Segmentation | Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Wenxiu Sun | Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between the current and past frames, which lead to mismatching to similar objects and high computational complexity. To address these problems, we propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet). In RMNet, the precise regional memory is constructed by memorizing local regions where the target objects appear in the past frames. For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame. The proposed local-to-local matching effectively eliminates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12934 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-Localization in Large Scenes From Body-Mounted Sensors | Vladimir Guzov, Aymen Mir, Torsten Sattler, Gerard Pons-Moll | We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors. Using IMUs attached at the body limbs and a head mounted camera looking outwards, HPS fuses camera based self-localization with IMU-based human body tracking. The former provides drift-free but noisy position and orientation estimates while the latter is accurate in the short-term but subject to drift over longer periods of time. We show that our optimization-based integration exploits the benefits of the two, resulting in pose accuracy free of drift. Furthermore, we integrate 3D scene constraints into our optimization, such as foot contact with the ground, resulting in physically plausible motion. HPS complements more common third-person-based 3D pose estimation methods. It allows capturing larger recording volumes and longer periods of motion, and could be used for VR/AR applications where humans interact with the scene without requiring direct line of sight with an external camera, or to train agents that navigate and interact with the environment based on first-person visual input, like real humans. With HPS, we recorded a dataset of humans interacting with large 3D scenes (300-1000 sq.m) consisting of 7 subjects and more than 3 hours of diverse motion. The dataset, code and video will be available on the project page: http://virtualhumans.mpi-inf.mpg.de/hps/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guzov_Human_POSEitioning_System_CVPR_2021_supplemental.pdf | null |
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection | Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, Marios Savvides | Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availability. In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection. Specifically, we represent each class concept by a semantic embedding learned from a large corpus of text. The detector is trained to project the image representations of objects into this embedding space. We also identify the problems of trivially using the raw embeddings with a heuristic knowledge graph and propose to augment the embeddings with a dynamic relation graph. As a result, our few-shot detector, termed SRR-FSD, is robust and stable to the variation of shots of novel objects. Experiments show that SRR-FSD can achieve competitive results at higher shots, and more importantly, a significantly better performance given both lower explicit and implicit shots. The benchmark protocol with implicit shots removed from the pretrained classification dataset can serve as a more realistic setting for future research. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Semantic_Relation_Reasoning_CVPR_2021_supplemental.pdf | null |
Online Multiple Object Tracking With Cross-Task Synergy | Song Guo, Jingya Wang, Xinchao Wang, Dacheng Tao | Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embeddings. Some works combined both directions within one framework but handled them as two individual tasks, thus gaining little mutual benefits. In this paper, we propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model. Specifically, the attention modules can make the prediction focus more on targets and less on distractors, therefore more reliable embeddings can be extracted accordingly for association. On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts. In this way, the synergy between position prediction and embedding association is achieved, which leads to strong robustness to occlusions. Extensive experiments demonstrate the superiority of our proposed model over a wide range of existing methods on MOTChallenge benchmarks. Our code and models are publicly available at https://github.com/songguocode/TADAM | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00380 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper.html | CVPR 2021 | null | null |
Discovering Relationships Between Object Categories via Universal Canonical Maps | Natalia Neverova, Artsiom Sanakoyeu, Patrick Labatut, David Novotny, Andrea Vedaldi | We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. However, training such models requires to initialize inter-category correspondences by hand. This is suboptimal and the resulting models fail to maintain correct correspondences as individual categories are learned. In this paper, we show that improved correspondences can be learned automatically as a natural byproduct of learning category-specific dense pose predictors. To do this, we express correspondences between different categories and between images and categories using a unified embedding. Then, we use the latter to enforce two constraints: symmetric inter-category cycle consistency and a new asymmetric image-to-category cycle consistency. Without any manual annotations for the inter-category correspondences, we obtain state-of-the-art alignment results, outperforming dedicated methods for matching 3D shapes. Moreover, the new model is also better at the task of dense pose prediction than prior work. | https://openaccess.thecvf.com/content/CVPR2021/papers/Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Neverova_Discovering_Relationships_Between_CVPR_2021_supplemental.pdf | null |
Prior Based Human Completion | Zibo Zhao, Wen Liu, Yanyu Xu, Xianing Chen, Weixin Luo, Lei Jin, Bohui Zhu, Tong Liu, Binqiang Zhao, Shenghua Gao | We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of learned priors to compensate for the damaged region. One is a structure prior, it uses a human parsing map to represent the human body structure. The other is a structure-texture correlation prior. It learns a structure and a texture memory bank, which encodes the common body structures and texture patterns, respectively. With the aid of these memory banks, the model could utilize the visible pattern to query and fetch a similar structure and texture pattern to introduce additional reasonable structures and textures for the corrupted region. Besides, since multiple potential human shapes are underlying the corrupted region, we propose multi-scale structure discriminators to further restore a plausible topological structure. Experiments on various large-scale benchmarks demonstrate the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Prior_Based_Human_Completion_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Prior_Based_Human_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Prior_Based_Human_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Prior_Based_Human_CVPR_2021_supplemental.pdf | null |
Neural Response Interpretation Through the Lens of Critical Pathways | Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab | Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective --- selecting the smallest group of neurons for which the response remains equivalent to the original network --- has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an L2-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code is publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Khakzar_Neural_Response_Interpretation_CVPR_2021_supplemental.pdf | null |
Rethinking and Improving the Robustness of Image Style Transfer | Pei Wang, Yijun Li, Nuno Vasconcelos | Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05623 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Rethinking_and_Improving_CVPR_2021_supplemental.pdf | null |
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding | Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang | Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Intersection-of-Union (IoU) scores are analogous to the intra-image augmentation used in contrastive visual representation learning. And we exploit this analogy and incorporate supervised contrastive learning to achieve more robust objects representations in FSOD. We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects. We notice the degradation of average precision (AP) for rare objects mainly comes from misclassifying novel instances as confusable classes. And we ease the misclassification issues by promoting instance level intra-class compactness and inter-class variance via our contrastive proposal encoding loss (CPE loss). Our design outperforms current state-of-the-art works in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL VOC and +2.7% on challenging COCO benchmark. Code is available at: https://github.com/ MegviiDetection/FSCE. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_FSCE_Few-Shot_Object_CVPR_2021_supplemental.pdf | null |
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains | Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai, Yumin Suh, Manmohan Chandraker | Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face Recognition, our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline methods and the state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07503 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification | Jiaxing Chen, Xinyang Jiang, Fudong Wang, Jun Zhang, Feng Zheng, Xing Sun, Wei-Shi Zheng | It is well acknowledged that person re-identification (person ReID) highly relies on visual texture information like clothing. Despite significant progress has been made in recent years, texture-confusing situations like clothing changing and persons wearing the same clothes receive little attention from most existing ReID methods. In this paper, rather than relying on texture based information, we propose to improve the robustness of person ReID against clothing texture by exploiting the information of a person's 3D shape. Existing shape learning schemas for person ReID either ignore the 3D information of a person, or require extra physical devices to collect 3D source data. Differently, we propose a novel ReID learning framework that directly extracts a texture-insensitive 3D shape embedding from a 2D image by adding 3D body reconstruction as an auxiliary task and regularization, called 3D Shape Learning (3DSL). The 3D reconstruction based regularization forces the ReID model to decouple the 3D shape information from the visual texture, and acquire discriminative 3D shape ReID features. To solve the problem of lacking 3D ground truth, we design an adversarial self-supervised projection (ASSP) model, performing 3D reconstruction without ground truth. Extensive experiments on common ReID datasets and texture-confusing datasets validate the effectiveness of our model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_3D_Shape_CVPR_2021_supplemental.pdf | null |
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources | Pengyu Li, Biao Wang, Lei Zhang | Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to train a fully connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities. This requires many computational resources, such as GPU memory. The metric learning paradigm is an economical computation method, but its performance is greatly inferior to that of the classification paradigm. To address this challenge, we propose a simple but effective CNN layer called the Virtual fully connected (Virtual FC) layer to reduce the computational consumption of the classification paradigm. Without bells and whistles, the proposed Virtual FC reduces the parameters by more than 100 times with respect to the fully connected layer and achieves competitive performance on mainstream face recognition evaluation datasets. Moreover, the performance of our Virtual FC layer on the evaluation datasets is superior to that of the metric learning paradigm by a significant margin. Our code will be released in hopes of disseminating our idea to other domains. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Virtual_Fully-Connected_Layer_CVPR_2021_supplemental.pdf | null |
Multi-Person Implicit Reconstruction From a Single Image | Armin Mustafa, Akin Caliskan, Lourdes Agapito, Adrian Hilton | We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffer from two main drawbacks: they are often model-based and therefore cannot capture accurate 3D models of people with loose clothing and hair; or they require manual intervention to resolve occlusions or interactions. Our method addresses both limitations by introducing the first end-to-end learning approach to perform model-free implicit reconstruction for realistic 3D capture of multiple clothed people in arbitrary poses (with occlusions) from a single image. Our network simultaneously estimates the 3D geometry of each person and their 6DOF spatial locations, to obtain a coherent multi-human reconstruction. In addition, we introduce a new synthetic dataset that depicts images with a varying number of inter-occluded humans in a variety of clothing and hair. We demonstrate robust, high-resolution reconstructions on images of multiple humans with complex occlusions, loose clothing and a large variety of poses, and scenes. Our quantitative evaluation on both synthetic and real world datasets demonstrates state-of-the-art performance with significant improvements in the accuracy and completeness of the reconstructions over competing approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09283 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection | Tingting Liang, Yongtao Wang, Zhi Tang, Guosheng Hu, Haibin Ling | Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy. Specifically, we first introduce six heterogeneous information paths to build our search space, namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect, and none. Second, we propose a novel search space of FPNs, in which each FPN candidate is represented by a densely-connected directed acyclic graph (each node is a feature pyramid and each edge is one of the six heterogeneous information paths). Third, we propose an efficient one-shot search method to find the optimal path aggregation architecture, that is, we first train a super-net and then find the optimal candidate with an evolutionary algorithm. Experimental results demonstrate the efficacy of the proposed OPANAS for object detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g., NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4 GPU days on MS-COCO); (2) the optimal architecture found by OPANAS significantly improves main-stream detectors including RetinaNet, Faster R-CNN and Cascade R-CNN, by 2.3 3.2 % mAP comparing to their FPN counterparts; and (3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at smaller training costs than comparable state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04507 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Bridge To Answer: Structure-Aware Graph Interaction Network for Video Question Answering | Jungin Park, Jiyoung Lee, Kwanghoon Sohn | This paper presents a novel method, termed Bridge to Answer, to infer correct answers for questions about a given video by leveraging adequate graph interactions of heterogeneous crossmodal graphs. To realize this, we learn question conditioned visual graphs by exploiting the relation between video and question to enable each visual node using question-to-visual interactions to encompass both visual and linguistic cues. In addition, we propose bridged visual-to-visual interactions to incorporate two complementary visual information on appearance and motion by placing the question graph as an intermediate bridge. This bridged architecture allows reliable message passing through compositional semantics of the question to generate an appropriate answer. As a result, our method can learn the question conditioned visual representations attributed to appearance and motion that show powerful capability for video question answering. Extensive experiments prove that the proposed method provides effective and superior performance than state-of-the-art methods on several benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14085 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Bridge_To_Answer_CVPR_2021_supplemental.pdf | null |
Learning Compositional Radiance Fields of Dynamic Human Heads | Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhofer | Photorealistic rendering of dynamic humans is an important ability for telepresence systems, virtual shopping, synthetic data generation, and more. Recently, neural rendering methods, which combine techniques from computer graphics and machine learning, have created high-fidelity models of humans and objects. Some of these methods do not produce results with high-enough fidelity for driveable human models (Neural Volumes) whereas others have extremely long rendering times (NeRF). We propose a novel compositional 3D representation that combines the best of previous methods to produce both higher-resolution and faster results. Our representation bridges the gap between discrete and continuous volumetric representations by combining a coarse 3D-structure-aware grid of animation codes with a continuous learned scene function that maps every position and its corresponding local animation code to its view-dependent emitted radiance and local volume density. Differentiable volume rendering is employed to compute photo-realistic novel views of the human head and upper body as well as to train our novel representation end-to-end using only 2D supervision. In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code. Our approach achieves state-of-the-art results for synthesizing novel views of dynamic human heads and the upper body. See our project page for more results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09955 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Learning_Compositional_Radiance_CVPR_2021_supplemental.pdf | null |
Partial Person Re-Identification With Part-Part Correspondence Learning | Tianyu He, Xu Shen, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua | Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Partial_Person_Re-Identification_CVPR_2021_supplemental.pdf | null |
Monte Carlo Scene Search for 3D Scene Understanding | Shreyas Hampali, Sinisa Stekovic, Sayan Deb Sarkar, Chetan S. Kumar, Friedrich Fraundorfer, Vincent Lepetit | We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data. More exactly, we propose a modification of the Monte Carlo Tree Search (MCTS) algorithm to retrieve objects and room layouts from noisy RGB-D scans. While MCTS was developed as a game-playing algorithm, we show it can also be used for complex perception problems. Our adapted MCTS algorithm has few easy-to-tune hyperparameters and can optimise general losses. We use it to optimise the posterior probability of objects and room layout hypotheses given the RGB-D data. This results in an analysis-by-synthesis approach that explores the solution space by rendering the current solution and comparing it to the RGB-D observations. To perform this exploration even more efficiently, we propose simple changes to the standard MCTS' tree construction and exploration policy. We demonstrate our approach on the ScanNet dataset. Our method often retrieves configurations that are better than some manual annotations, especially on layouts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07969 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hampali_Monte_Carlo_Scene_CVPR_2021_supplemental.pdf | null |
Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck | Anguo Zhang, Yueming Gao, Yuzhen Niu, Wenxi Liu, Yongcheng Zhou | Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis. However, due to the large intra-class variation of a person across cameras, e.g., occlusions, illuminations, viewpoints, and poses, Re-ID is still a challenging task in the field of computer vision. In this paper, to attack the issues concerning with intra-class variation, we propose a coarse-to-fine Re-ID framework with the incorporation of auxiliary-domain classification (ADC) and second-order information bottleneck (2O-IB). In particular, as an auxiliary task, ADC is introduced to extract the coarse-grained essential features to distinguish a person from miscellaneous backgrounds, which leads to the effective coarse- and fine-grained feature representations for Re-ID. On the other hand, to cope with the redundancy, irrelevance, and noise contained in the Re-ID features caused by intra-class variations, we integrate 2O-IB into the network to compress and optimize the features, without increasing additional computation overhead during inference. Experimental results demonstrate that our proposed method significantly reduces the neural network output variance of intra-class person images and achieves the superior performance to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Coarse-To-Fine_Person_Re-Identification_CVPR_2021_supplemental.pdf | null |
Transformer Tracking | Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, Huchuan Lu | Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Transformer_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15436 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Transformer_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Transformer_Tracking_CVPR_2021_paper.html | CVPR 2021 | null | null |
Structured Multi-Level Interaction Network for Video Moment Localization via Language Query | Hao Wang, Zheng-Jun Zha, Liang Li, Dong Liu, Jiebo Luo | We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment construction for both cross-modal understanding and video content comprehension, which are the two crucial challenges for this task. In this paper, we disentangle the activity moment into boundary and content. Based on the explored moment structure, we propose a novel Structured Multi-level Interaction Network (SMIN) to tackle this problem through multi-levels of cross-modal interaction coupled with content-boundary-moment interaction. In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interact the word-level query with content and boundary, as in a coarse-to-fine manner. For content-boundary-moment interaction, we capture the insightful relations between boundary, content, and the whole moment proposal. Through multi-level interactions, the model obtains robust cross-modal representation for accurate moment localization. Extensive experiments conducted on three benchmarks (i.e., Charades-STA, ActivityNet-Captions, and TACoS) demonstrate the proposed approach outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper.html | CVPR 2021 | null | null |
Structured Scene Memory for Vision-Language Navigation | Hanqing Wang, Wenguan Wang, Wei Liang, Caiming Xiong, Jianbing Shen | Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03454 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Unsupervised Pre-Training for Person Re-Identification | Dengpan Fu, Dongdong Chen, Jianmin Bao, Hao Yang, Lu Yuan, Lei Zhang, Houqiang Li, Dong Chen | In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re-ID datasets are all of limited scale due to the costly effort required for data annotation. Previous research tries to leverage models pre-trained on ImageNet to mitigate the shortage of person Re-ID data but suffers from the large domain gap between ImageNet and person Re-ID data. LUPerson is an unlabeled dataset of 4M images of over 200K identities, which is 30xlarger than the largest existing Re-ID dataset. It also covers a much diverse range of capturing environments (e.g., camera settings, scenes, etc.). Based on this dataset, we systematically study the key factors for learning Re-ID features from two perspectives: data augmentation and contrastive loss. Unsupervised pre-training performed on this large-scale dataset effectively leads to a generic Re-ID feature that can benefit all existing person Re-ID methods. Using our pre-trained model in some basic frameworks, our methods achieve state-of-the-art results without bells and whistles on four widely used Re-ID datasets: CUHK03, Market1501, DukeMTMC, and MSMT17. Our results also show that the performance improvement is more significant on small-scale target datasets or under few-shot setting. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03753 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Unsupervised_Pre-Training_for_CVPR_2021_supplemental.pdf | null |
Progressive Stage-Wise Learning for Unsupervised Feature Representation Enhancement | Zefan Li, Chenxi Liu, Alan Yuille, Bingbing Ni, Wenjun Zhang, Wen Gao | Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the objective. In this work, we explore new dimensions of unsupervised learning by proposing the Progressive Stage-wise Learning (PSL) framework. For a given unsupervised task, we design multi-level tasks and define different learning stages for the deep network. Early learning stages are forced to focus on low-level tasks while late stages are guided to extract deeper information through harder tasks. We discover that by progressive stage-wise learning, unsupervised feature representation can be effectively enhanced. Our extensive experiments show that PSL consistently improves results for the leading unsupervised learning methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05554 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper.html | CVPR 2021 | null | null |
Domain-Specific Suppression for Adaptive Object Detection | Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu | Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. Current UDA object detection methods view the two directions as a whole while optimizing, which will cause domain-invariant direction mismatch even if the output features are perfectly aligned. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation to detach the two parts of directions and suppress the domain-specific one. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including weather, camera configuration, and synthetic to real-world adaptation. Our experiment results show significant advance over the state-of-the-art methods in the UDA object detection field, performing a promotion of 10.2 12.2% mAP on all these domain adaptation scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.03570 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Few-Shot Object Detection via Classification Refinement and Distractor Retreatment | Yiting Li, Haiyue Zhu, Yu Cheng, Wenxin Wang, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee | We aim to tackle the challenging Few-Shot Object Detection (FSOD) where data-scarce categories are presented during the model learning. The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a novel aspect of hard example mining. Specifically, to address the intrinsic architecture limitation of common detectors under low-data constraint, we introduce a novel few-shot classification refinement mechanism where a decoupled Few-Shot Classification Network (FSCN) is employed to improve the classification. Moreover, we specially probe a commonly-overlooked but destructive issue of FSOD, i.e., the presence of distractor samples due to the incomplete annotations where images from base set may contain novel-class objects but remain unlabelled. Retreatment solutions are developed to eliminate the incurred false positives. For FSCN training, the distractor is formulated as a semi-supervised problem, where a distractor utilization loss is proposed to make proper use of it for boosting the data-scarce classes; while a Self-Supervised Dataset Pruning (SSDP) technique is developed to facilitate the few-shot adaptation of base detector. Experiments demonstrate that our proposed framework achieves the state-of-the-art FSOD performance on public datasets, e.g., Pascal VOC and MS-COCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Few-Shot_Object_Detection_CVPR_2021_supplemental.pdf | null |
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images | Manyi Li, Hao Zhang | We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D^2IM_Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_D2IM-Net_Learning_Detail_CVPR_2021_supplemental.pdf | null |
Not Just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction | Daejin Kim, Mohammad Azam Khan, Jaegul Choo | Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper.html | CVPR 2021 | null | null |
Behavior-Driven Synthesis of Human Dynamics | Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Bjorn Ommer | Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively. Project page is available at https://cutt.ly/5l7rXEp | https://openaccess.thecvf.com/content/CVPR2021/papers/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Blattmann_Behavior-Driven_Synthesis_of_CVPR_2021_supplemental.zip | null |
GAIA: A Transfer Learning System of Object Detection That Fits Your Needs | Xingyuan Bu, Junran Peng, Junjie Yan, Tieniu Tan, Zhaoxiang Zhang | Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA, which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains, and collecting relevant data for practitioners who have very few datapoints for their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, Caltech, CityPersons, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells. To benefit every practitioner in the community of object detection, we would release our pre-trained models and code. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.11346 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bu_GAIA_A_Transfer_CVPR_2021_supplemental.pdf | null |
IronMask: Modular Architecture for Protecting Deep Face Template | Sunpill Kim, Yunseong Jeong, Jinsu Kim, Jungkon Kim, Hyung Tae Lee, Jae Hong Seo | Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed. In this paper, we present a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric. We circumvent the need for binarization, which is the main cause of performance degradation in most existing face template protections, by proposing a new real-valued error-correcting-code that is compatible with real-valued templates and can therefore, minimize performance degradation. We evaluate the efficacy of IronMask by extensive experiments on two face recognitions, ArcFace and CosFace with three datasets, CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results, IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate (FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with CosFace, while providing at least 115-bit security against known attacks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_IronMask_Modular_Architecture_CVPR_2021_supplemental.pdf | null |
Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild | Zhaoyuan Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanling Zhang, Shenghua Gao | This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms. Our data, code, and models are available at https://github.com/svip-lab/IVOS-W. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Learning_To_Recommend_CVPR_2021_supplemental.pdf | null |
DSRNA: Differentiable Search of Robust Neural Architectures | Ramtin Hosseini, Xingyi Yang, Pengtao Xie | In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture to change prediction outcomes significantly. To address this problem, we propose methods to perform differentiable searches of robust neural architectures. In our methods, two differentiable metrics are defined to measure architectures' robustness, based on certified lower bound and Jacobian norm bound. Then we search for robust architectures by maximizing the robustness metrics. Different from previous approaches which aim to improve architectures' robustness in an implicit way: performing adversarial training and injecting random noise, our methods explicitly and directly maximize robustness metrics to harvest robust architectures. On CIFAR-10, ImageNet, and MNIST, we perform game-based evaluation and verification-based evaluation on the robustness of our methods. The experimental results show that our methods 1) are more robust to various norm-bound attacks than several robust NAS baselines; 2) are more accurate than baselines when there are no attacks; 3) have significantly higher certified lower bounds than baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper.html | CVPR 2021 | null | null |
Reconstructing 3D Human Pose by Watching Humans in the Mirror | Qi Fang, Qing Shuai, Junting Dong, Hujun Bao, Xiaowei Zhou | In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity. We develop an optimization-based approach that exploits mirror symmetry constraints for accurate 3D pose reconstruction. We also provide a method to estimate the surface normal of the mirror from vanishing points in the single image. To validate the proposed approach, we collect a large-scale dataset named Mirrored-Human, which covers a large variety of human subjects, poses and backgrounds. The experiments demonstrate that, when trained on Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the accuracy and generalizability of existing single-view 3D pose estimators can be largely improved. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream | Jing Zhao, Ruiqin Xiong, Hangfan Liu, Jian Zhang, Tiejun Huang | The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process. For spike cameras, image reconstruction remains an important and challenging issue. To this end, this paper develops a spike-to-image neural network (Spk2ImgNet) to reconstruct the dynamic scene from the continuous spike stream. In particular, to handle the challenges brought by both noise and high-speed motion, we propose a hierarchical architecture to exploit the temporal correlation of the spike stream progressively. Firstly, a spatially adaptive light inference subnet is proposed to exploit the local temporal correlation, producing basic light intensity estimates of different moments. Then, a pyramid deformable alignment is utilized to align the intermediate features such that the feature fusion module can exploit the long-term temporal correlation, while avoiding undesired motion blur. In addition, to train the network, we simulate the working mechanism of spike camera to generate a large-scale spike dataset composed of spike streams and corresponding ground truth images. Experimental results demonstrate that the proposed network evidently outperforms the state-of-the-art spike camera reconstruction methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Spk2ImgNet_Learning_To_CVPR_2021_supplemental.zip | null |
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation | Hansheng Chen, Yuyao Huang, Wei Tian, Zhong Gao, Lu Xiong | Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D model and then estimating object pose via Perspective-n-Point (PnP) algorithm can achieve remarkable localization accuracy. Yet these methods rely on training with ground truth of object geometry, which is difficult to acquire in real outdoor scenes. To address this issue, we propose MonoRUn, a novel detection framework that learns dense correspondences and geometry in a self-supervised manner, with simple 3D bounding box annotations. To regress the pixel-related 3D object coordinates, we employ a regional reconstruction network with uncertainty awareness. For self-supervised training, the predicted 3D coordinates are projected back to the image plane. A Robust KL loss is proposed to minimize the uncertainty-weighted reprojection error. During testing phase, we exploit the network uncertainty by propagating it through all downstream modules. More specifically, the uncertainty-driven PnP algorithm is leveraged to estimate object pose and its covariance. Extensive experiments demonstrate that our proposed approach outperforms current state-of-the-art methods on KITTI benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_MonoRUn_Monocular_3D_CVPR_2021_supplemental.pdf | null |
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds | Li Yi, Boqing Gong, Thomas Funkhouser | We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain training pairs for SVCN requires no manual labeling. We also introduce local adversarial learning to model the surface prior. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors. Experiments and ablation studies with our new benchmark for cross-domain semantic labeling of LiDAR data show that the proposed approach provides 6.3-37.6% better performance than previous domain adaptation methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.08488 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yi_Complete__Label_CVPR_2021_supplemental.pdf | null |
GMOT-40: A Benchmark for Generic Multiple Object Tracking | Hexin Bai, Wensheng Cheng, Peng Chu, Juehuan Liu, Kai Zhang, Haibin Ling | Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to boost the study of GMOT in three aspects. First, we construct the first publicly available dense GMOT dataset, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories. In addition, two tracking protocols are adopted to evaluate different characteristics of tracking algorithms. Second, by noting the lack of devoted tracking algorithms, we have designed a series of baseline GMOT algorithms. Third, we perform thorough evaluations on GMOT-40, involving popular MOT algorithms (with necessary modifications) and the proposed baselines. The GMOT-40 benchmark is publicly available at https://github.com/Spritea/GMOT40. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_GMOT-40_A_Benchmark_CVPR_2021_supplemental.pdf | null |
Few-Shot Image Generation via Cross-Domain Correspondence | Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang | Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06820 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ojha_Few-Shot_Image_Generation_CVPR_2021_supplemental.pdf | null |
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation | Tommi Kerola, Jie Li, Atsushi Kanehira, Yasunori Kudo, Alexis Vallet, Adrien Gaidon | Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. In contrast, we propose Hierarchical Lovasz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals. Besides modeling instances precisely in a proposal-free manner, our Hierarchical Lovasz Embeddings generalize to categories by using a simple Nearest-Class-Mean classifier, including for non-instance ""stuff"" classes where instance segmentation methods are not applicable. Our simple model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas. Furthermore, our model demonstrates temporal stability between video frames. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04555 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kerola_Hierarchical_Lovasz_Embeddings_CVPR_2021_supplemental.pdf | null |
Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans | Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou | This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Experiments on a newly collected multi-view dataset show that our approach outperforms prior works by a large margin in terms of the novel view synthesis quality. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset. We will release the code and dataset for reproducibility. | https://openaccess.thecvf.com/content/CVPR2021/papers/Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper.html | CVPR 2021 | null | null |
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting | Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin | Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited information of RGB images and cannot well discover potential pedestrians in unconstrained scenarios. In this work, we find that incorporating optical and thermal information can greatly help to recognize pedestrians. To promote future researches in this field, we introduce a large-scale RGBT Crowd Counting (RGBT-CC) benchmark, which contains 2,030 pairs of RGB-thermal images with 138,389 annotated people. Furthermore, to facilitate the multimodal crowd counting, we propose a cross-modal collaborative representation learning framework, which consists of multiple modality-specific branches, a modality-shared branch, and an Information Aggregation-Distribution Module (IADM) to capture the complementary information of different modalities fully. Specifically, our IADM incorporates two collaborative information transfers to dynamically enhance the modality-shared and modality-specific representations with a dual information propagation mechanism. Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting. Moreover, the proposed approach is universal for multimodal crowd counting and is also capable to achieve superior performance on the ShanghaiTechRGBD dataset. Finally, our source code and benchmark have been released at http://lingboliu.com/RGBT_Crowd_Counting.html. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Cross-Modal_Collaborative_Representation_CVPR_2021_supplemental.pdf | null |
Weakly Supervised Video Salient Object Detection | Wangbo Zhao, Jing Zhang, Long Li, Nick Barnes, Nian Liu, Junwei Han | Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are timeconsuming and expensive to obtain. To relieve the burden of data annotation, we present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations". Specifically, an "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on our new weak annotations. Further, we design a novel foreground-background similarity loss to further explore the labeling similarity across frames. A weak annotation boosting strategy is also introduced to boost our model performance with a new pseudo-label generation technique. Extensive experimental results on six benchmark video saliency detection datasets illustrate the effectiveness of our solution. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Weakly_Supervised_Video_CVPR_2021_supplemental.pdf | null |
Pixel-Wise Anomaly Detection in Complex Driving Scenes | Giancarlo Di Biase, Hermann Blum, Roland Siegwart, Cesar Cadena | The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous areas or re-synthesizing the image from the semantic label map to find dissimilarities with the input image. In this work, we demonstrate that these two methodologies contain complementary information and can be combined to produce robust predictions for anomaly segmentation. We present a pixel-wise anomaly detection framework that uses uncertainty maps to improve over existing re-synthesis methods in finding dissimilarities between the input and generated images. Our approach works as a general framework around already trained segmentation networks, which ensures anomaly detection without compromising segmentation accuracy, while significantly outperforming all similar methods. Top-2 performance across a range of different anomaly datasets shows the robustness of our approach to handling different anomaly instances. | https://openaccess.thecvf.com/content/CVPR2021/papers/Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05445 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Di_Biase_Pixel-Wise_Anomaly_Detection_CVPR_2021_supplemental.pdf | null |
Learning To Associate Every Segment for Video Panoptic Segmentation | Sanghyun Woo, Dahun Kim, Joon-Young Lee, In So Kweon | Temporal correspondence -- linking pixels or objects across frames -- is a fundamental supervisory signal for the video models. For the panoptic understanding of dynamic scenes, we further extend this concept to every segment. Specifically, we aim to learn coarse segment-level matching and fine pixel-level matching together. We implement this idea by designing two novel learning objectives. To validate our proposals, we adopt a deep siamese model and train the model to learn the temporal correspondence on two different levels (i.e., segment and pixel) along with the target task. At inference time, the model processes each frame independently without any extra computation and post-processing. We show that our per-frame inference model can achieve new state-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to its high efficiency, the model runs in a fraction of time (3x) compared to the previous state-of-the-art approach. The codes and models will be released. | https://openaccess.thecvf.com/content/CVPR2021/papers/Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09453 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Variational Transformer Networks for Layout Generation | Diego Martin Arroyo, Janis Postels, Federico Tombari | Generative models able to synthesize layouts of different kinds (e.g. documents, user interfaces or furniture arrangements) are a useful tool to aid design processes and as a first step in the generation of synthetic data, among other tasks. We exploit the properties of self-attention layers to capture high level relationships between elements in a layout, and use these as the building blocks of the well-known Variational Autoencoder (VAE) formulation. Our proposed Variational Transformer Network (VTN) is capable of learning margins, alignments and other global design rules without explicit supervision. Layouts sampled from our model have a high degree of resemblance to the training data, while demonstrating appealing diversity. In an extensive evaluation on publicly available benchmarks for different layout types VTNs achieve state-of-the-art diversity and perceptual quality. Additionally, we show the capabilities of this method as part of a document layout detection pipeline. | https://openaccess.thecvf.com/content/CVPR2021/papers/Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Arroyo_Variational_Transformer_Networks_CVPR_2021_supplemental.zip | null |
Mitigating Face Recognition Bias via Group Adaptive Classifier | Sixue Gong, Xiaoming Liu, Anil K. Jain | Face recognition is known to exhibit bias -- subjects in a certain demographic group can be better recognized than other groups. This work aims to learn a fair face representation, where faces of every group could be more equally represented. Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes. The adaptive module comprises kernel masks and channel-wise attention maps for each demographic group so as to activate different facial regions for identification, leading to more discriminative features pertinent to their demographics. Our introduced automated adaptation strategy determines whether to apply adaptation to a certain layer by iteratively computing the dissimilarity among demographic-adaptive parameters. A new de-biasing loss function is proposed to mitigate the gap of average intra-class distance between demographic groups. Experiments on face benchmarks (RFW, LFW, IJB-A, and IJB-C) show that our work is able to mitigate face recognition bias across demographic groups while maintaining the competitive accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.07576 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_Mitigating_Face_Recognition_CVPR_2021_supplemental.pdf | null |
A Peek Into the Reasoning of Neural Networks: Interpreting With Structural Visual Concepts | Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, Ziyan Wu | Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability. While recent developments in explainable artificial intelligence attempt to bridge this gap (e.g., by visualizing the correlation between input pixels and final outputs), these approaches are limited to explaining low-level relationships, and crucially, do not provide insights on error correction. In this work, we propose a framework (VRX) to interpret classification NNs with intuitive structural visual concepts. Given a trained classification model, the proposed VRX extracts relevant class-specific visual concepts and organizes them using structural concept graphs (SCG) based on pairwise concept relationships. By means of knowledge distillation, we show VRX can take a step towards mimicking the reasoning process of NNs and provide logical, concept-level explanations for final model decisions. With extensive experiments, we empirically show VRX can meaningfully answer "why" and "why not" questions about the prediction, providing easy-to-understand insights about the reasoning process. We also show that these insights can potentially provide guidance on improving NN's performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.00290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_A_Peek_Into_CVPR_2021_supplemental.pdf | null |
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations | Zhihui Li, Lina Yao | Temporal action detection on unconstrained videos has seen significant research progress in recent years. Deep learning has achieved enormous success in this direction. However, collecting large-scale temporal detection datasets to ensuring promising performance in the real-world is a laborious, impractical and time consuming process. Accordingly, we present a novel improved temporal action localization model that is better able to take advantage of limited labeled data available. Specifically, we design two auxiliary tasks by reconstructing the available label information and then facilitate the learning of the temporal action detection model. Each task generates their supervision signal by recycling the original annotations, and are jointly trained with the temporal action detection model in a multi-task learning fashion. Note that the proposed approach can be pluggable to any region proposal based temporal action detection models. We conduct extensive experiments on three benchmark datasets, namely THUMOS'14, Charades and ActivityNet. Our experimental results confirm the effectiveness of the proposed model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper.html | CVPR 2021 | null | null |
A Dual Iterative Refinement Method for Non-Rigid Shape Matching | Rui Xiang, Rongjie Lai, Hongkai Zhao | In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration. In each DIR iteration, starting from current correspondence, a zoom-in process at each point is used to select well matched anchor pairs by a local mapping distortion criterion. These selected anchor pairs are then used to align spectral features (or other appropriate global features) whose dimension adaptively matches the capacity of the selected anchor pairs. Thanks to the effective combination of complementary information in a data-adaptive way, DIR is not only efficient but also robust to render accurate results within a few iterations. By choosing appropriate dual features, DIR has the flexibility to handle patch and partial matching as well. Extensive experiments on various data sets demonstrate the superiority of DIR over other state-of-the-art methods in terms of both accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.13049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiang_A_Dual_Iterative_CVPR_2021_supplemental.pdf | null |
Image Super-Resolution With Non-Local Sparse Attention | Yiqun Mei, Yuchen Fan, Yuqian Zhou | Both non-local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies NL attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mei_Image_Super-Resolution_With_CVPR_2021_supplemental.pdf | null |
3D Video Stabilization With Depth Estimation by CNN-Based Optimization | Yao-Chih Lee, Kuan-Wei Tseng, Yu-Ta Chen, Chien-Cheng Chen, Chu-Song Chen, Yi-Ping Hung | Video stabilization is an essential component of visual quality enhancement. Early methods rely on feature tracking to recover either 2D or 3D frame motion, which suffer from the robustness of local feature extraction and tracking in shaky videos. Recently, learning-based methods seek to find frame transformations with high-level information via deep neural networks to overcome the robustness issue of feature tracking. Nevertheless, to our best knowledge, no learning-based methods leverage 3D cues for the transformation inference yet; hence they would lead to artifacts on complex scene-depth scenarios. In this paper, we propose Deep3D Stabilizer, a novel 3D depth-based learning method for video stabilization. We take advantage of the recent self-supervised framework on jointly learning depth and camera ego-motion estimation on raw videos. Our approach requires no data for pre-training but stabilizes the input video via 3D reconstruction directly. The rectification stage incorporates the 3D scene depth and camera motion to smooth the camera trajectory and synthesize the stabilized video. Unlike most one-size-fits-all learning-based methods, our smoothing algorithm allows users to manipulate the stability of a video efficiently. Experimental results on challenging benchmarks show that the proposed solution consistently outperforms the state-of-the-art methods on almost all motion categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_3D_Video_Stabilization_CVPR_2021_supplemental.pdf | null |
Predicting Human Scanpaths in Visual Question Answering | Xianyu Chen, Ming Jiang, Qi Zhao | Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Predicting_Human_Scanpaths_CVPR_2021_supplemental.pdf | null |
DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution | Siyuan Qiao, Liang-Chieh Chen, Alan Yuille | Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.02334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.html | CVPR 2021 | null | null |
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks | Shunsuke Saito, Jinlong Yang, Qianli Ma, Michael J. Black | We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. These avatars are driven by pose parameters and have realistic clothing that moves and deforms naturally. SCANimate does not rely on a customized mesh template or surface mesh registration. We observe that fitting a parametric 3D body model, like SMPL, to a clothed human scan is tractable while surface registration of the body topology to the scan is often not, because clothing can deviate significantly from the body shape. We also observe that articulated transformations are invertible, resulting in geometric cycle-consistency in the posed and unposed shapes. These observations lead us to a weakly supervised learning method that aligns scans into a canonical pose by disentangling articulated deformations without template-based surface registration. Furthermore, to complete missing regions in the aligned scans while modeling pose-dependent deformations, we introduce a locally pose-aware implicit function that learns to complete and model geometry with learned pose correctives. In contrast to commonly used global pose embeddings, our local pose conditioning significantly reduces long-range spurious correlations and improves generalization to unseen poses, especially when training data is limited. Our method can be applied to pose-aware appearance modeling to generate a fully textured avatar. We demonstrate our approach on various clothing types with different amounts of training data, outperforming existing solutions and other variants in terms of fidelity and generality in every setting. The code is available at https://scanimate.is.tue.mpg.de | https://openaccess.thecvf.com/content/CVPR2021/papers/Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03313 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saito_SCANimate_Weakly_Supervised_CVPR_2021_supplemental.pdf | null |
Improving Accuracy of Binary Neural Networks Using Unbalanced Activation Distribution | Hyungjun Kim, Jihoon Park, Changhun Lee, Jae-Joon Kim | Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. Several techniques were proposed to improve the accuracy of BNNs. One of the approaches is to balance the distribution of binary activations so that the amount of information in the binary activations becomes maximum. Based on extensive analysis, in stark contrast to previous work, we argue that unbalanced activation distribution can actually improve the accuracy of BNNs. We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models. Experimental results show that the accuracy of previous BNN models (e.g. XNOR-Net and Bi-Real-Net) can be improved by simply shifting the threshold values of binary activation functions without requiring any other modification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Improving_Accuracy_of_CVPR_2021_supplemental.pdf | null |
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation | Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, Dahua Lin | State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution. Although this corporation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. Moreover, a point-wise refinement module is introduced to alleviate the interference of lossy voxel-based label encoding. We evaluate the proposed model on two large-scale datasets , i.e., SemanticKITTI and nuScenes. Our method achieves the 1st place in the leaderboard of SemanticKITTI and outperforms existing methods on nuScenes with a noticeable margin. Furthermore, the proposed 3D framework also generalizes well to LiDAR panoptic segmentation and LiDAR 3D detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10033 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
SMPLicit: Topology-Aware Generative Model for Clothed People | Enric Corona, Albert Pumarola, Guillem Alenya, Gerard Pons-Moll, Francesc Moreno-Noguer | In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at https://link/smplicit/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06871 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Corona_SMPLicit_Topology-Aware_Generative_CVPR_2021_supplemental.pdf | null |
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization | Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu | We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. We further propose two regularization terms to ensure disentanglement and smoothness of the learned representations. The resulting pose representations can be used for cross-view action recognition. To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition. This task trains models with actions from only one single viewpoint while models are evaluated on poses captured from all possible viewpoints. We evaluate the learned representations on standard benchmarks for action recognition, and show that (i) CV-MIM performs competitively compared with the state-of-the-art models in the fully-supervised scenarios; (ii) CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting; (iii) and the learned representations can significantly boost the performance when reducing the amount of supervised training data. Our code is made publicly available at https://github.com/google-research/google-research/tree/master/poem. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01405 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Learning_View-Disentangled_Human_CVPR_2021_supplemental.pdf | null |
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation | Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, Jian Zhang | Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14581 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation | Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Xiaoliang Cheng, Kewei Liang | Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28*28 binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In this paper, we propose a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector. Our method, termed DCT-Mask, could be easily integrated into most pixel-based instance segmentation methods. Without any bells and whistles, DCT-Mask yields significant gains on different frameworks, backbones, datasets, and training schedules. It does not require any pre-processing or pre-training, and almost no harm to the running speed. Especially, for higher-quality annotations and more complex backbones, our method has a greater improvement. Moreover, we analyze the performance of our method from the perspective of the quality of mask representation. The main reason why DCT-Mask works well is that it obtains a high-quality mask representation with low complexity. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Bridging the Visual Gap: Wide-Range Image Blending | Chia-Ni Lu, Ya-Chu Chang, Wei-Chen Chiu | In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, image outpainting, and image blending, none of the approaches from these topics is able to easily address it. We introduce an effective deep-learning model to realize wide-range image blending, where a novel Bidirectional Content Transfer module is proposed to perform the conditional prediction for the feature representation of the intermediate region via recurrent neural networks. In addition to ensuring the spatial and semantic consistency during the blending, we also adopt the contextual attention mechanism as well as the adversarial learning scheme in our proposed method for improving the visual quality of the resultant panorama. We experimentally demonstrate that our proposed method is not only able to produce visually appealing results for wide-range image blending, but also able to provide superior performance with respect to several baselines built upon the state-of-the-art image inpainting and outpainting approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Bridging_the_Visual_CVPR_2021_supplemental.pdf | null |
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification | Jong-Chyi Su, Zezhou Cheng, Subhransu Maji | We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature. | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Su_A_Realistic_Evaluation_CVPR_2021_supplemental.pdf | null |
Residential Floor Plan Recognition and Reconstruction | Xiaolei Lv, Shengchu Zhao, Xinyang Yu, Binqiang Zhao | Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields. An automatic framework is provided that accurately recognizes the structure, type, and size of the room, and outputs vectorized 3D reconstruction results. Deep segmentation and detection neural networks are utilized to extract room structural information. Key points detection network and cluster analysis are utilized to calculate scales of rooms. The vectorization of room information is processed through an iterative optimization-based method. The system significantly increases accuracy and generalization ability, compared with existing methods. It outperforms other systems in floor plan segmentation and vectorization process, especially inclined wall detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Residential_Floor_Plan_CVPR_2021_supplemental.pdf | null |
Dynamic Domain Adaptation for Efficient Inference | Shuang Li, JinMing Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li | Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer "easier" and "harder" target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers; 2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16403 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.html | CVPR 2021 | null | null |
Regularization Strategy for Point Cloud via Rigidly Mixed Sample | Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee | Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.01929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Regularization_Strategy_for_CVPR_2021_supplemental.pdf | null |
StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision | Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, Hujun Bao | In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction. Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details. Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality. Consequently, our StereoPIFu can naturally infer the human body's spatial location in camera space and maintain the correct relative position of different parts of the human body, which enables our method to capture human performance. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction, which is demonstrated by extensive experimental results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper.html | CVPR 2021 | null | null |
Unsupervised Multi-Source Domain Adaptation Without Access to Source Data | Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury | Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled dataset by transferring knowledge from a labeled source data, which has been trained on similar tasks. However, most of these conventional UDA approaches have a strong assumption of having access to the source data during training, which may not be very practical due to privacy, security and storage concerns. A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain only from a single learned source model without requiring access to the source data. However, for adaptation purpose, if there are multiple trained source models available to choose from, this method has to go through adapting each and every model individually, to check for the best source. Thus, we ask the question: can we find the optimal combination of source models, with no source data and without target labels, whose performance is no worse than the single best source? To answer this, we propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model. We provide intuitive theoretical insights to justify our claim. Moreover, extensive experiments are conducted on several benchmark datasets to show the effectiveness of our algorithm, where in most cases, our method not only reaches best source accuracy but also outperform it. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmed_Unsupervised_Multi-Source_Domain_CVPR_2021_supplemental.pdf | null |
On Semantic Similarity in Video Retrieval | Michael Wray, Hazel Doughty, Dima Damen | Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. We demonstrate that this assumption results in performance comparisons often not indicative of models' retrieval capabilities. We propose a move to semantic similarity video retrieval, where (i) multiple videos/captions can be deemed equally relevant, and their relative ranking does not affect a method's reported performance and (ii) retrieved videos/captions are ranked by their similarity to a query. We propose several proxies to estimate semantic similarities in large-scale retrieval datasets, without additional annotations. Our analysis is performed on three commonly used video retrieval datasets (MSR-VTT, YouCook2 and EPIC-KITCHENS). | https://openaccess.thecvf.com/content/CVPR2021/papers/Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wray_On_Semantic_Similarity_CVPR_2021_supplemental.pdf | null |
Few-Shot Open-Set Recognition by Transformation Consistency | Minki Jeong, Seokeon Choi, Changick Kim | In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method utilizes pseudo-unseen class samples, which are collected from the other dataset or synthesized samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jeong_Few-Shot_Open-Set_Recognition_CVPR_2021_supplemental.pdf | null |
Uncertainty-Guided Model Generalization to Unseen Domains | Fengchun Qiao, Xi Peng | We study a worst-case scenario in generalization: Out-of-domain generalization from a single source. The goal is to learn a robust model from a single source and expect it to generalize over many unknown distributions. This challenging problem has been seldom investigated while existing solutions suffer from various limitations. In this paper, we propose a new solution. The key idea is to augment the source capacity in both input and label spaces, while the augmentation is guided by uncertainty assessment. To the best of our knowledge, this is the first work to (1) access the generalization uncertainty from a single source and (2) leverage it to guide both input and label augmentation for robust generalization. The model training and deployment are effectively organized in a Bayesian meta-learning framework. We conduct extensive comparisons and ablation study to validate our approach. The results prove our superior performance in a wide scope of tasks including image classification, semantic segmentation, text classification, and speech recognition. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiao_Uncertainty-Guided_Model_Generalization_CVPR_2021_supplemental.pdf | null |
Debiased Subjective Assessment of Real-World Image Enhancement | Peibei Cao, Zhangyang Wang, Kede Ma | In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space; 2) algorithmic bias due to potential overfitting the selected samples; 3) subjective bias due to further potential cherry-picking test results. This eventually makes the field of real-world image enhancement more of an art than a science. Here we take steps towards debiasing conventional subjective assessment by automatically sampling a set of adaptive and diverse images for subsequent testing. This is achieved by casting sample selection into a joint maximization of the discrepancy between the enhancers and the diversity among the selected input images. Careful visual inspection on the resulting enhanced images provides a debiased ranking of the enhancement algorithms. We demonstrate our subjective assessment method using three popular and practically demanding image enhancement tasks: dehazing, super-resolution, and low-light enhancement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.10080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.html | CVPR 2021 | null | null |
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search | Kaicheng Yu, Rene Ranftl, Mathieu Salzmann | Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This violates the main assumption of weight-sharing NAS algorithms, thus limiting their effectiveness. We tackle this issue by proposing a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures using a small set of landmark architectures. We incorporate our regularization term into three different NAS algorithms and show that it consistently improves performance across algorithms, search-spaces, and tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05309 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Landmark_Regularization_Ranking_CVPR_2021_supplemental.pdf | null |
Noise-Resistant Deep Metric Learning With Ranking-Based Instance Selection | Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao | The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving robustness to noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains open. In this paper, we propose a noise-resistant training technique for DML, which we name Probabilistic Ranking-based Instance Selection with Memory (PRISM). PRISM identifies noisy data in a minibatch using average similarity against image features extracted by several previous versions of the neural network. These features are stored in and retrieved from a memory bank. To alleviate the high computational cost brought by the memory bank, we introduce an acceleration method that replaces individual data points with the class centers. In extensive comparisons with 12 existing approaches under both synthetic and real-world label noise, PRISM demonstrates superior performance of up to 6.06% in Precision@1. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Noise-Resistant_Deep_Metric_CVPR_2021_supplemental.pdf | null |
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation | Hugo Germain, Vincent Lepetit, Guillaume Bourmaud | Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r.t. the camera pose, the sum of so-called Reprojection Errors (RE). We argue that generating putative 2D-3D correspondences 1) leads to an important loss of information that needs to be compensated as far as possible, within RE, through the choice of a robust loss and the tuning of its hyperparameters and 2) may lead to an RE that conveys erroneous data to the pose estimator. In this paper, we introduce the Neural Reprojection Error (NRE) as a substitute for RE. NRE allows to rethink the camera pose estimation problem by merging it with the feature learning problem, hence leveraging richer information than 2D-3D correspondences and eliminating the need for choosing a robust loss and its hyperparameters. Thus NRE can be used as training loss to learn image descriptors tailored for pose estimation. We also propose a coarse-to-fine optimization method able to very efficiently minimize a sum of NRE terms w.r.t. the camera pose. We experimentally demonstrate that NRE is a good substitute for RE as it significantly improves both the robustness and the accuracy of the camera pose estimate while being computationally and memory highly efficient. From a broader point of view, we believe this new way of merging deep learning and 3D geometry may be useful in other computer vision applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07153 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Germain_Neural_Reprojection_Error_CVPR_2021_supplemental.pdf | null |
Cross Modal Focal Loss for RGBD Face Anti-Spoofing | Anjith George, Sebastien Marcel | Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complementary information from the two modalities while reducing the impact of overfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.00948 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.html | CVPR 2021 | null | null |
StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks | Kai Fischer, Martin Simon, Florian Olsner, Stefan Milz, Horst-Michael Gross, Patrick Mader | Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much better results, but suffer from a heavy runtime. We overcome these drawbacks by introducing StickyPillars, a fast, accurate and extremely robust deep middle-end 3D feature matching method on point clouds. It uses graph neural networks and performs context aggregation on sparse 3D key-points with the aid of transformer based multi-head self and cross-attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. The system does not rely on hand crafted feature descriptors or heuristic matching strategies. We present state-of-art art accuracy results on the registration problem demonstrated on the KITTI dataset while being four times faster then leading deep methods. Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset. Finally, we demonstrate robustness on KITTI odometry. Our method remains stable in accuracy where state-of-the-art procedures fail on frame drops and higher speeds. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.html | CVPR 2021 | null | null |
HoHoNet: 360 Indoor Holistic Understanding With Latent Horizontal Features | Cheng Sun, Min Sun, Hwann-Tzong Chen | We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHoNet advances in two important aspects. First, the deep architecture is redesigned to run faster with improved accuracy. Second, we propose a novel horizon-to-dense module, which relaxes the per-column output shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoNet is fast: It runs at 52 FPS and 110 FPS with ResNet-50 and ResNet-34 backbones respectively, for modeling dense modalities from a high-resolution 512x1024 panorama. HoHoNet is also accurate. On the tasks of layout estimation and semantic segmentation, HoHoNet achieves results on par with current state-of-the-art. On dense depth estimation, HoHoNet outperforms all the prior arts by a large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_HoHoNet_360_Indoor_CVPR_2021_supplemental.pdf | null |
Online Learning of a Probabilistic and Adaptive Scene Representation | Zike Yan, Xin Wang, Hongbin Zha | Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene with a Bayesian nonparametric mixture model, seamlessly describing per-point occupancy status with a continuous probability density function. Instead of following the conventional data fusion paradigm, we address the problem of online learning the process how sequential point cloud data are generated from the scene geometry. An incremental and parallel inference is performed to update the parameter space in real-time. We experimentally show that the proposed representation achieves state-of-the-art accuracy with promising efficiency. The consistent probabilistic formulation assures a generative model that is adaptive to different sensor characteristics, and the model complexity can be dynamically adjusted on-the-fly according to different data scales. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16832 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Domain Adaptation With Auxiliary Target Domain-Oriented Classifier | Jian Liang, Dapeng Hu, Jiashi Feng | Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present generic semi-supervised learning (SSL) techniques and directly apply them to DA tasks, even achieving competitive performance. One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data via the classifier trained by labeled data. However, it ignores the distribution shift in DA problems and is inevitably biased to source data. To address this issue, we propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels. Specifically, we employ the memory mechanism and develop two types of non-parametric classifiers, i.e. the nearest centroid classifier and neighborhood aggregation, without introducing any additional network parameters. Despite its simplicity in a pseudo classification objective, ATDOC with neighborhood aggregation significantly outperforms domain alignment techniques and prior SSL techniques on a large variety of DA benchmarks and even scare-labeled SSL tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.04171 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning To Recover 3D Scene Shape From a Single Image | Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, Chunhua Shen | Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail and propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then use 3D point cloud encoders to predict the missing depth shift and focal length that allow us to recover a realistic 3D scene shape. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to enhance depth prediction models trained on mixed datasets. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot dataset generalization. Code is available at:https://git.io/Depth. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09365 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Learning_To_Recover_CVPR_2021_supplemental.pdf | null |
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes | Zhengqi Li, Simon Niklaus, Noah Snavely, Oliver Wang | We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Neural_Scene_Flow_CVPR_2021_supplemental.pdf | null |
FS-Net: Fast Shape-Based Network for Category-Level 6D Object Pose Estimation With Decoupled Rotation Mechanism | Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Linlin Shen, Ales Leonardis | In this paper, we focus on category-level 6D pose and size estimation from a monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction, which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. First, we design an orientation aware autoencoder with 3D graph convolution for latent feature extraction. Thanks to the shift and scale-invariance properties of 3D graph convolution, the learned latent feature is insensitive to point shift and object size. Then, to efficiently decode category-level rotation information from the latent feature, we propose a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information. For translation and size, we estimate them by two residuals: the difference between the mean of object points and ground truth translation, and the difference between the mean size of the category and ground truth size, respectively. Finally, to increase the generalization ability of the FS-Net, we propose an online box-cage based 3D deformation mechanism to augment the training data. Extensive experiments on two benchmark datasets show that the proposed method achieves state-of-the-art performance in both category- and instance-level 6D object pose estimation. Especially in category-level pose estimation, without extra synthetic data, our method outperforms existing methods by 6.3% on the NOCS-REAL dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_FS-Net_Fast_Shape-Based_CVPR_2021_supplemental.pdf | null |
Unsupervised Human Pose Estimation Through Transforming Shape Templates | Luca Schmidtke, Athanasios Vlontzos, Simon Ellershaw, Anna Lukens, Tomoki Arichi, Bernhard Kainz | Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to generalize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. | https://openaccess.thecvf.com/content/CVPR2021/papers/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Schmidtke_Unsupervised_Human_Pose_CVPR_2021_supplemental.zip | null |
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship | Jing Wang, Jinhui Tang, Mingkun Yang, Xiang Bai, Jiebo Luo | OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OCR tokens. Existing methods attempt to achieve this goal via encoding the OCR tokens with rich visual and semantic representations. However, strong correlations between OCR tokens may not be established with such limited representations. In this paper, we propose to enhance the connections between OCR tokens from the viewpoint of exploiting the geometrical relationship. We comprehensively consider the height, width, distance, IoU and orientation relations between the OCR tokens for constructing the geometrical relationship. To integrate the learned relation as well as the visual and semantic representations into a unified framework, a Long Short-Term Memory plus Relation-aware pointer network (LSTM-R) architecture is presented in this paper. Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning. Extensive experiments demonstrate the effectiveness of our LSTM-R. More remarkably, LSTM-R achieves state-of-the-art performance on TextCaps, with the CIDEr-D score being increased from 98.0% to 109.3%. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper.html | CVPR 2021 | null | null |
Cross-Iteration Batch Normalization | Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin | A well-known issue of Batch Normalization is its significantly reduced effectiveness in the case of small mini-batch sizes. When a mini-batch contains few examples, the statistics upon which the normalization is defined cannot be reliably estimated from it during a training iteration. To address this problem, we present Cross-Iteration Batch Normalization (CBN), in which examples from multiple recent iterations are jointly utilized to enhance estimation quality. A challenge of computing statistics over multiple iterations is that the network activations from different iterations are not comparable to each other due to changes in network weights. We thus compensate for the network weight changes via a proposed technique based on Taylor polynomials, so that the statistics can be accurately estimated and batch normalization can be effectively applied. On object detection and image classification with small mini-batch sizes, CBN is found to outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2002.05712 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_supplemental.pdf | null |
Multimodal Contrastive Training for Visual Representation Learning | Xin Yuan, Zhe Lin, Jason Kuen, Jianming Zhang, Yilin Wang, Michael Maire, Ajinkya Kale, Baldo Faieta | We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously, hence improving the quality of learned visual representations. By including multimodal training in a unified framework with different types of contrastive losses, our method can learn more powerful and generic visual features. We first train our model on COCO and evaluate the learned visual representations on various downstream tasks including image classification, object detection, and instance segmentation. For example, the visual representations pre-trained on COCO by our method achieve state-of-the-art top-1 validation accuracy of 55.3% on ImageNet classification, under the common transfer protocol. We also evaluate our method on the large-scale Stock images dataset and show its effectiveness on multi-label image tagging, and cross-modal retrieval tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
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