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title string | authors string | abstract string | pdf string | arXiv string | bibtex string | url string | detail_url string | tags string | supp string | string |
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DualGraph: A Graph-Based Method for Reasoning About Label Noise | HaiYang Zhang, XiMing Xing, Liang Liu | Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take f... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.html | CVPR 2021 | null | null |
Automatic Correction of Internal Units in Generative Neural Networks | Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi | Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are kno... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06118 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tousi_Automatic_Correction_of_CVPR_2021_supplemental.pdf | null |
Generating Manga From Illustrations via Mimicking Manga Creation Workflow | Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu | We present a framework to generate manga from digital illustrations. In professional mange studios, the manga create workflow consists of three key steps: (1) Artists use line drawings to delineate the structural outlines in manga storyboards. (2) Artists apply several types of regular screentones to render the shading... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training | Yinglong Wang, Chao Ma, Bing Zeng | Existing deep deraining models are mainly learned via directly minimizing the statistical differences between rainy images and rain-free ground truths. They emphasize learning a mapping from rainy images to rain-free images with supervision. Despite the demonstrated success, these methods do not perform well on restori... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.html | CVPR 2021 | null | null |
Open-Vocabulary Object Detection Using Captions | Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, Shih-Fu Chang | Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been ex... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zareian_Open-Vocabulary_Object_Detection_CVPR_2021_supplemental.pdf | null |
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization | Xingjia Pan, Yingguo Gao, Zhiwen Lin, Fan Tang, Weiming Dong, Haolei Yuan, Feiyue Huang, Changsheng Xu | Weakly supervised object localization (WSOL) remains an open problem due to the deficiency of finding object extent information using a classification network. While prior works struggle to localize objects by various spatial regularization strategies, we argue that how to extract object structural information from the... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_Unveiling_the_Potential_CVPR_2021_supplemental.pdf | null |
From Points to Multi-Object 3D Reconstruction | Francis Engelmann, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari | We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. The key idea is to optimize for detection, alignment and shape jointly over all objects in the RGB image, while focusing on realistic and physically plausible reconstructions. To this end, we propose a key-point detector that loc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11575 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dual-Stream Multiple Instance Learning Network for Whole Slide Image Classification With Self-Supervised Contrastive Learning | Bin Li, Yin Li, Kevin W. Eliceiri | We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classificat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08939 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Dual-Stream_Multiple_Instance_CVPR_2021_supplemental.pdf | null |
Regressive Domain Adaptation for Unsupervised Keypoint Detection | Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long | Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classification settings and may fail in regression tasks, especially in the practical keypoint detection task. To ta... | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mask Guided Matting via Progressive Refinement Network | Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, Alan Yuille | We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask pert... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06722 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
Monocular Reconstruction of Neural Face Reflectance Fields | Mallikarjun B R, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, Christian Theobalt | The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a sp... | https://openaccess.thecvf.com/content/CVPR2021/papers/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.10247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/R_Monocular_Reconstruction_of_CVPR_2021_supplemental.pdf | null |
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network | Xu Yang, Cheng Deng, Zhiyuan Dang, Kun Wei, Junchi Yan | Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-Shot Learning | Chaofan Chen, Xiaoshan Yang, Changsheng Xu, Xuhui Huang, Zhe Ma | Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.08523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Coarse-Fine Networks for Temporal Activity Detection in Videos | Kumara Kahatapitiya, Michael S. Ryoo | In this paper, we introduce 'Coarse-Fine Networks', a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion. Traditional Video models process inputs at one (or few) fixed temporal resolution without any dynamic frame selectio... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
Can Audio-Visual Integration Strengthen Robustness Under Multimodal Attacks? | Yapeng Tian, Chenliang Xu | In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whethe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02000 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Can_Audio-Visual_Integration_CVPR_2021_supplemental.pdf | null |
Deep Gradient Projection Networks for Pan-sharpening | Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, Chunxia Zhang | Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.html | CVPR 2021 | null | null |
ReNAS: Relativistic Evaluation of Neural Architecture Search | Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang, Shangling Jui, Chunjing Xu, Chang Xu | An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing NAS algorithms often train and evaluate intermediate neural architectures on a small proxy dataset with limited training epochs. But it is ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/1910.01523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_ReNAS_Relativistic_Evaluation_CVPR_2021_supplemental.pdf | null |
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks | Jiahang Wang, Sheng Jin, Wentao Liu, Weizhong Liu, Chen Qian, Ping Luo | Human pose estimation is a fundamental yet challenging task in computer vision, which aims at localizing human anatomical keypoints. However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions. This work comprehens... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_When_Human_Pose_CVPR_2021_supplemental.pdf | null |
ReMix: Towards Image-to-Image Translation With Limited Data | Jie Cao, Luanxuan Hou, Ming-Hsuan Yang, Ran He, Zhenan Sun | Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel c... | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cao_ReMix_Towards_Image-to-Image_CVPR_2021_supplemental.pdf | null |
Adaptive Rank Estimate in Robust Principal Component Analysis | Zhengqin Xu, Rui He, Shoulie Xie, Shiqian Wu | Robust principal component analysis (RPCA) and its variants have gained wide applications in computer vision. However, these methods either involve manual adjustment of some parameters, or require the rank of a low-rank matrix to be known a prior. In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is propos... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Adaptive_Rank_Estimate_CVPR_2021_supplemental.zip | null |
Continual Adaptation of Visual Representations via Domain Randomization and Meta-Learning | Riccardo Volpi, Diane Larlus, Gregory Rogez | Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature -- the well-known "catastrophic forgetting" issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past domains in favor of ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Volpi_Continual_Adaptation_of_CVPR_2021_supplemental.pdf | null |
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance | Kaihua Zhang, Mingliang Dong, Bo Liu, Xiao-Tong Yuan, Qingshan Liu | The objective of co-saliency detection is to segment the co-occurring salient objects in a group of images. To address this task, we introduce a new deep network architecture via semantic-aware contrast Gromov-Wasserstein distance (DeepACG). We first adopt the Gromov-Wasserstein (GW) distance to build dense hierarchica... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.html | CVPR 2021 | null | null |
SurFree: A Fast Surrogate-Free Black-Box Attack | Thibault Maho, Teddy Furon, Erwan Le Merrer | Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals. Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.html | CVPR 2021 | null | null |
Beyond Image to Depth: Improving Depth Prediction Using Echoes | Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma | We address the problem of estimating depth with multi modal audio visual data. Inspired by the ability of animals, such as bats and dolphins, to infer distance of objects with echolocation, some recent methods have utilized echoes for depth estimation. We propose an end-to-end deep learning based pipeline utilizing RGB... | https://openaccess.thecvf.com/content/CVPR2021/papers/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Parida_Beyond_Image_to_CVPR_2021_supplemental.pdf | null |
Rich Features for Perceptual Quality Assessment of UGC Videos | Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang | Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively invest... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Rich_Features_for_CVPR_2021_supplemental.pdf | null |
Sequential Graph Convolutional Network for Active Learning | Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim | We propose a novel pool-based Active Learning frame-work constructed on a sequential Graph Convolution Net-work (GCN). Each image's feature from a pool of data rep-resents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.10219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Caramalau_Sequential_Graph_Convolutional_CVPR_2021_supplemental.pdf | null |
Generative Classifiers as a Basis for Trustworthy Image Classification | Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, Carsten Rother | With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainability and robustness. Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities. Howe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.15036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mackowiak_Generative_Classifiers_as_CVPR_2021_supplemental.pdf | null |
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation | Yang Jiao, Trac D. Tran, Guangming Shi | This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow F, stereo-depth D, camera pose P and motion segmentation S. Our key insight is that the rigidity of the scene shares the same inherent geometrical structure with object movem... | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.html | CVPR 2021 | null | null |
Localizing Visual Sounds the Hard Way | Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman | The objective of this work is to localize sound sources that are visible in a video without using manual annotations. Our key technical contribution is to show that, by training the network to explicitly discriminate challenging image fragments, even for images that do contain the object emitting the sound, we can sign... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02691 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.html | CVPR 2021 | null | null |
Synthesize-It-Classifier: Learning a Generative Classifier Through Recurrent Self-Analysis | Arghya Pal, Raphael C.-W. Phan, KokSheik Wong | In this work, we show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the data distribution a... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pal_Synthesize-It-Classifier_Learning_a_CVPR_2021_supplemental.pdf | null |
Self-Point-Flow: Self-Supervised Scene Flow Estimation From Point Clouds With Optimal Transport and Random Walk | Ruibo Li, Guosheng Lin, Lihua Xie | Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Self-Point-Flow_Self-Supervised_Scene_CVPR_2021_supplemental.pdf | null |
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation | Yunhang Shen, Liujuan Cao, Zhiwei Chen, Feihong Lian, Baochang Zhang, Chi Su, Yongjian Wu, Feiyue Huang, Rongrong Ji | Panoptic segmentation aims to partition an image to object instances and semantic content for thing and stuff categories, respectively. To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored. In this paper, we propose an efficient jointly thing-and-stuff mining ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals | Yiyue Luo, Yunzhu Li, Michael Foshey, Wan Shou, Pratyusha Sharma, Tomas Palacios, Antonio Torralba, Wojciech Matusik | Daily human activities, e.g., locomotion, exercises, and resting, are heavily guided by the tactile interactions between the human and the ground. In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input. We build a ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Intelligent_Carpet_Inferring_CVPR_2021_supplemental.zip | null |
Railroad Is Not a Train: Saliency As Pseudo-Pixel Supervision for Weakly Supervised Semantic Segmentation | Seungho Lee, Minhyun Lee, Jongwuk Lee, Hyunjung Shim | Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, namely Explicit Pseudo-pixel ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.08965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Railroad_Is_Not_CVPR_2021_supplemental.zip | null |
Stable View Synthesis | Gernot Riegler, Vladlen Koltun | We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Riegler_Stable_View_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.07233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Riegler_Stable_View_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Riegler_Stable_View_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Deep Two-View Structure-From-Motion Revisited | Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, Hongdong Li | Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. Existing deep learning-based approaches formulate the problem in ways that are fundamentally ill-posed, relying on training data to overcome the inherent difficulties. In contrast, we propose a return to the basics. We revisit... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Deep_Two-View_Structure-From-Motion_CVPR_2021_supplemental.zip | null |
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes | Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht, Bjorn Ommer | There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17185 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kotovenko_Rethinking_Style_Transfer_CVPR_2021_supplemental.pdf | null |
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation | Rui Gong, Yuhua Chen, Danda Pani Paudel, Yawei Li, Ajad Chhatkuli, Wen Li, Dengxin Dai, Luc Van Gool | Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains. In this work, we propose a principled meta-learning based approach to OCDA for semantic se... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_Cluster_Split_Fuse_CVPR_2021_supplemental.pdf | null |
Beyond Short Clips: End-to-End Video-Level Learning With Collaborative Memories | Xitong Yang, Haoqi Fan, Lorenzo Torresani, Larry S. Davis, Heng Wang | The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label. We argue that a single clip may not have enough temporal coverage to exhibit the label to recognize, since video datasets are often weakly lab... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.html | CVPR 2021 | null | null |
PointDSC: Robust Point Cloud Registration Using Deep Spatial Consistency | Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, Chiew-Lan Tai | Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has receive... | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05465 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_PointDSC_Robust_Point_CVPR_2021_supplemental.pdf | null |
Task Programming: Learning Data Efficient Behavior Representations | Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona | Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video track... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13917 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Task_Programming_Learning_CVPR_2021_supplemental.pdf | null |
ACRE: Abstract Causal REasoning Beyond Covariation | Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, Yixin Zhu | Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in variou... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_ACRE_Abstract_Causal_CVPR_2021_supplemental.pdf | null |
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition | Jingwei Huang, Shan Huang, Mingwei Sun | We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new bac... | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_DeepLM_Large-Scale_Nonlinear_CVPR_2021_supplemental.pdf | null |
TDN: Temporal Difference Networks for Efficient Action Recognition | Limin Wang, Zhan Tong, Bin Ji, Gangshan Wu | Temporal modeling still remains challenging for action recognition in videos. To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition. The core of our TDN is to devise a... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_TDN_Temporal_Difference_CVPR_2021_supplemental.pdf | null |
LiBRe: A Practical Bayesian Approach to Adversarial Detection | Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu | Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve this problem, but they typically demonstrate restricted practicability owing to unsurmountable compromise on universality, effectiveness, or ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_LiBRe_A_Practical_CVPR_2021_supplemental.pdf | null |
ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes | Hao Su, Jianwei Niu, Xuefeng Liu, Qingfeng Li, Ji Wan, Mingliang Xu, Tao Ren | Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works typically beatif... | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Supervised Pillar Motion Learning for Autonomous Driving | Chenxu Luo, Xiaodong Yang, Alan Yuille | Autonomous driving can benefit from motion behavior comprehension when interacting with diverse traffic participants in highly dynamic environments. Recently, there has been a growing interest in estimating class-agnostic motion directly from point clouds. Current motion estimation methods usually require vast amount o... | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Self-Supervised_Pillar_Motion_CVPR_2021_supplemental.pdf | null |
Quantum Permutation Synchronization | Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas J. Guibas | We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.07755 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_supplemental.pdf | null |
QAIR: Practical Query-Efficient Black-Box Attacks for Image Retrieval | Xiaodan Li, Jinfeng Li, Yuefeng Chen, Shaokai Ye, Yuan He, Shuhui Wang, Hang Su, Hui Xue | We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query access to the top-k ranked unlabeled images from the database. Compared with query attacks in image classification, which produce adversaries ac... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02927 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_QAIR_Practical_Query-Efficient_CVPR_2021_supplemental.zip | null |
MagFace: A Universal Representation for Face Recognition and Quality Assessment | Qiang Meng, Shichao Zhao, Zhida Huang, Feng Zhou | The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meng_MagFace_A_Universal_CVPR_2021_supplemental.pdf | null |
Wasserstein Barycenter for Multi-Source Domain Adaptation | Eduardo Fernandes Montesuma, Fred Maurice Ngole Mboula | Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distribution. To overcome the challenges posed by this learning scenario, we propose a method for constructing an intermediate domain between sources and target domain, the Wasserstein Barycenter ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Montesuma_Wasserstein_Barycenter_for_CVPR_2021_supplemental.pdf | null |
Unsupervised Hyperbolic Metric Learning | Jiexi Yan, Lei Luo, Cheng Deng, Heng Huang | Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning. Following the paradigm in supervised manner, most existing unsupervised metric learning approaches mainly focus on binary similarity in Euclidean s... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Improving Sign Language Translation With Monolingual Data by Sign Back-Translation | Hao Zhou, Wengang Zhou, Weizhen Qi, Junfu Pu, Houqiang Li | Despite existing pioneering works on sign language translation (SLT), there is a non-trivial obstacle, i.e., the limited quantity of parallel sign-text data. To tackle this parallel data bottleneck, we propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training.... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12397 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Background Splitting: Finding Rare Classes in a Sea of Background | Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian | We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accur... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.12873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mullapudi_Background_Splitting_Finding_CVPR_2021_supplemental.pdf | null |
Adaptive Convolutions for Structure-Aware Style Transfer | Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Markus Gross, Derek Bradley | Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properti... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chandran_Adaptive_Convolutions_for_CVPR_2021_supplemental.zip | null |
Few-Shot Incremental Learning With Continually Evolved Classifiers | Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, Yinghui Xu | Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Few-Shot_Incremental_Learning_CVPR_2021_supplemental.pdf | null |
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions | Junbin Xiao, Xindi Shang, Angela Yao, Tat-Seng Chua | We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting at causal action reasoning, temporal action reasoning and common scen... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_NExT-QA_Next_Phase_CVPR_2021_supplemental.pdf | null |
LayoutGMN: Neural Graph Matching for Structural Layout Similarity | Akshay Gadi Patil, Manyi Li, Matthew Fisher, Manolis Savva, Hao Zhang | We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize wea... | https://openaccess.thecvf.com/content/CVPR2021/papers/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Patil_LayoutGMN_Neural_Graph_CVPR_2021_supplemental.pdf | null |
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search | Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li | Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more effi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_TransNAS-Bench-101_Improving_Transferability_CVPR_2021_supplemental.pdf | null |
ArtEmis: Affective Language for Visual Art | Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas J. Guibas | We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the aff... | https://openaccess.thecvf.com/content/CVPR2021/papers/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.07396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sketch, Ground, and Refine: Top-Down Dense Video Captioning | Chaorui Deng, Shizhe Chen, Da Chen, Yuan He, Qi Wu | The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the def... | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Normal Dynamics in Videos With Meta Prototype Network | Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, Jian Yang | Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones. Previous methods introduced the memory bank into AE, for en... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06689 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Learning_Normal_Dynamics_CVPR_2021_supplemental.pdf | null |
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition | Yifan Zhao, Ke Yan, Feiyue Huang, Jia Li | Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrel... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
Normal Integration via Inverse Plane Fitting With Minimum Point-to-Plane Distance | Xu Cao, Boxin Shi, Fumio Okura, Yasuyuki Matsushita | This paper presents a surface normal integration method that solves an inverse problem of local plane fitting. Surface reconstruction from normal maps is essential in photometric shape reconstruction. To this end, we formulate normal integration in the camera coordinates and jointly solve for 3D point positions and loc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cao_Normal_Integration_via_CVPR_2021_supplemental.pdf | null |
NPAS: A Compiler-Aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration | Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin | With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently, and do... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00596 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spatial Feature Calibration and Temporal Fusion for Effective One-Stage Video Instance Segmentation | Minghan Li, Shuai Li, Lida Li, Lei Zhang | Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level insta... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05606 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Spatial_Feature_Calibration_CVPR_2021_supplemental.pdf | null |
Learning Asynchronous and Sparse Human-Object Interaction in Videos | Romero Morais, Vuong Le, Svetha Venkatesh, Truyen Tran | Human activities can be learned from video. With effective modeling it is possible to discover not only the action labels but also the temporal structure of the activities, such as the progression of the sub-activities. Automatically recognizing such structure from raw video signal is a new capability that promises aut... | https://openaccess.thecvf.com/content/CVPR2021/papers/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morais_Learning_Asynchronous_and_CVPR_2021_supplemental.pdf | null |
Single Image Reflection Removal With Absorption Effect | Qian Zheng, Boxin Shi, Jinnan Chen, Xudong Jiang, Ling-Yu Duan, Alex C. Kot | In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly ta... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Single_Image_Reflection_CVPR_2021_supplemental.pdf | null |
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking | Minghao Chen, Jianlong Fu, Haibin Ling | Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired b... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_One-Shot_Neural_Ensemble_CVPR_2021_supplemental.pdf | null |
Disentangled Cycle Consistency for Highly-Realistic Virtual Try-On | Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu, Ping Luo | Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation netwo... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.09479 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Disentangled_Cycle_Consistency_CVPR_2021_supplemental.pdf | null |
M3DSSD: Monocular 3D Single Stage Object Detector | Shujie Luo, Hang Dai, Ling Shao, Yong Ding | In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the... | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13164 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.html | CVPR 2021 | null | null |
Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes | Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie Zhou | Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer f... | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Structure-Aware_Face_Clustering_CVPR_2021_supplemental.pdf | null |
Objects Are Different: Flexible Monocular 3D Object Detection | Yunpeng Zhang, Jiwen Lu, Jie Zhou | The precise localization of 3D objects from a single image without depth information is a highly challenging problem. Most existing methods adopt the same approach for all objects regardless of their diverse distributions, leading to limited performance especially for truncated objects. In this paper, we propose a flex... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification | Oren Nuriel, Sagie Benaim, Lior Wolf | Recent work has shown that convolutional neural network classifiers overly rely on texture at the expense of shape cues. We make a similar but different distinction between shape and local image cues, on the one hand, and global image statistics, on the other. Our method, called Permuted Adaptive Instance Normalization... | https://openaccess.thecvf.com/content/CVPR2021/papers/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.05785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pixel Codec Avatars | Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De la Torre, Yaser Sheikh | Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances. In this work, we present the Pixel Codec Avatars (PiCA): a deep generative model of 3D human faces that achieves state of the art rec... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Pixel_Codec_Avatars_CVPR_2021_supplemental.zip | null |
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification | Zijian Hu, Zhengyu Yang, Xuefeng Hu, Ram Nevatia | A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information not only from labeled data but also from a ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16725 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_SimPLE_Similar_Pseudo_CVPR_2021_supplemental.pdf | null |
Context-Aware Layout to Image Generation With Enhanced Object Appearance | Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang | A layout to image (L2I) generation model aims to generate a complicated image containing multiple objects (things) against natural background (stuff), conditioned on a given layout. Built upon the recent advances in generative adversarial networks (GANs), recent L2I models have made great progress. However, a close ins... | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis | Yi Liu, Xiaoyang Huo, Tianyi Chen, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong | Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available. Learning precise class semantics is crucial for class-conditional image synthesis with limited supervision... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
LEAP: Learning Articulated Occupancy of People | Marko Mihajlovic, Yan Zhang, Michael J. Black, Siyu Tang | Substantial progress has been made on modeling rigid 3D objects using deep implicit representations. Yet, extending these methods to learn neural models of human shape is still in its infancy. Human bodies are complex and the key challenge is to learn a representation that generalizes such that it can express body shap... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06849 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mihajlovic_LEAP_Learning_Articulated_CVPR_2021_supplemental.pdf | null |
ANR: Articulated Neural Rendering for Virtual Avatars | Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner | Deferred Neural Rendering (DNR) uses a three-step pipeline to translate a mesh representation into an RGB image. The combination of a traditional rendering stack with neural networks hits a sweet spot in terms of computational complexity and realism of the resulting images. Using skinned meshes for animatable objects i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.12890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Raj_ANR_Articulated_Neural_CVPR_2021_supplemental.pdf | null |
Flow-Based Kernel Prior With Application to Blind Super-Resolution | Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte | Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization losses to constrain the kernel space. However, they fail to fully e... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_Flow-Based_Kernel_Prior_CVPR_2021_supplemental.pdf | null |
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services | Jinyu Tian, Jiantao Zhou, Jia Duan | Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to use... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12344 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Probabilistic_Selective_Encryption_CVPR_2021_supplemental.pdf | null |
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images | Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn | Humans perceive and construct the surrounding world as an arrangement of simple parametric models. In particular, man-made environments commonly consist of volumetric primitives such as cuboids or cylinders. Inferring these primitives is an important step to attain high-level, abstract scene descriptions. Previous appr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kluger_Cuboids_Revisited_Learning_CVPR_2021_supplemental.pdf | null |
Dive Into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition | Jiahui She, Yibo Hu, Hailin Shi, Jun Wang, Qiu Shen, Tao Mei | Due to the subjective annotation and the inherent inter-class similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Di... | https://openaccess.thecvf.com/content/CVPR2021/papers/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/She_Dive_Into_Ambiguity_CVPR_2021_supplemental.pdf | null |
Attention-Guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton | Xi Zhang, Xiaolin Wu | We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Attention-Guided_Image_Compression_CVPR_2021_supplemental.pdf | null |
Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering | Huafeng Liu, Jiaqi Wang, Liping Jing | In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac). It provides an efficient neural clustering architecture by grouping data points in a cluster-wise view rather than point-wise view. CHiGac simultaneously learns what makes a cluster, how to group data points int... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Cluster-Wise_Hierarchical_Generative_CVPR_2021_supplemental.pdf | null |
Mirror3D: Depth Refinement for Mirror Surfaces | Jiaqi Tan, Weijie Lin, Angel X. Chang, Manolis Savva | Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We th... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Mirror3D_Depth_Refinement_CVPR_2021_supplemental.pdf | null |
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning | Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, Han Hu | Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10043 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Reciprocal Transformations for Unsupervised Video Object Segmentation | Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He | Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and r... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_Reciprocal_Transformations_for_CVPR_2021_supplemental.zip | null |
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark | Longyin Wen, Dawei Du, Pengfei Zhu, Qinghua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu | To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured large-scale dataset, named as DroneCrowd, formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800 people trajectories w... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Complete 3D Morphable Face Models From Images and Videos | Mallikarjun B R, Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt | Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity and expression geometry, and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expression... | https://openaccess.thecvf.com/content/CVPR2021/papers/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.01679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/R_Learning_Complete_3D_CVPR_2021_supplemental.pdf | null |
Bottom-Up Shift and Reasoning for Referring Image Segmentation | Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu | Referring image segmentation aims to segment the referent that is the corresponding object or stuff referred by a natural language expression in an image. Its main challenge lies in how to effectively and efficiently differentiate between the referent and other objects of the same category as the referent. In this pape... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion | Vitor Guizilini, Rares Ambrus, Wolfram Burgard, Adrien Gaidon | Estimating scene geometry from cost-effective sensors is key for robots. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Auxiliary Networks (SAN), a new module enabling monodepth n... | https://openaccess.thecvf.com/content/CVPR2021/papers/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.html | CVPR 2021 | null | null |
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes With Biharmonic Coordinates | Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su | We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.09105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_DeepMetaHandles_Learning_Deformation_CVPR_2021_supplemental.pdf | null |
Panoptic Segmentation Forecasting | Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander G. Schwing | Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents which need not only passively analyze an observation but also must react to it in real-time. Importantly, accurate forecasting hinges upon ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_supplemental.zip | null |
SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection | Weichen Zhang, Wen Li, Dong Xu | Geometric characteristic plays an important role in the representation of an object in 3D point clouds. For example, large objects often contain more points, while small ones contain fewer points. The point clouds of objects near the capture device are denser, while those of distant objects are sparser. These issues br... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pedestrian and Ego-Vehicle Trajectory Prediction From Monocular Camera | Lukas Neumann, Andrea Vedaldi | Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e.g. breaking) to take place. In this paper, we propose a new method to predict future positi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.html | CVPR 2021 | null | null |
Globally Optimal Relative Pose Estimation With Gravity Prior | Yaqing Ding, Daniel Barath, Jian Yang, Hui Kong, Zuzana Kukelova | Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freed... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00458 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ding_Globally_Optimal_Relative_CVPR_2021_supplemental.pdf | null |
Mutual CRF-GNN for Few-Shot Learning | Shixiang Tang, Dapeng Chen, Lei Bai, Kaijian Liu, Yixiao Ge, Wanli Ouyang | Graph-neural-networks (GNN) is a rising trend for few-shot learning. A critical component in GNN is the affinity. Typically, affinity in GNN is mainly computed in the feature space, e.g., pairwise features, and does not take fully advantage of semantic labels associated to these features. In this paper, we propose a no... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Mutual_CRF-GNN_for_CVPR_2021_supplemental.pdf | null |
Weakly Supervised Action Selection Learning in Video | Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guangwei Yu | Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to tra... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | CVPR 2021 | null | null |
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