paper_id stringlengths 33 138 | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 305 619 | abstract large_stringlengths 449 1.99k |
|---|---|---|---|---|---|---|---|---|---|
Ruan_Gaussian_Context_Transformer_CVPR_2021_paper | Gaussian Context Transformer | [
"Dongsheng Ruan",
"Daiyin Wang",
"Yuan Zheng",
"Nenggan Zheng",
"Min Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ruan_Gaussian_Context_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ruan_Gaussian_Context_Transformer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ruan_Gaussian_Context_Transformer_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Ruan_2021_CVPR,
author = {Ruan, Dongsheng and Wang, Daiyin and Zheng, Yuan and Zheng, Nenggan and Zheng, Min},
title = {Gaussian Context Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Recently, a large number of channel attention blocks are proposed to boost the representational power of deep convolutional neural networks (CNNs). These approaches commonly learn the relationship between global contexts and attention activations by using fully-connected layers or linear transformations. However, we em... |
Zhang_Keypoint-Graph-Driven_Learning_Framework_for_Object_Pose_Estimation_CVPR_2021_paper | Keypoint-Graph-Driven Learning Framework for Object Pose Estimation | [
"Shaobo Zhang",
"Wanqing Zhao",
"Ziyu Guan",
"Xianlin Peng",
"Jinye Peng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Keypoint-Graph-Driven_Learning_Framework_for_Object_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Keypoint-Graph-Driven_Learning_Framework_for_Object_Pose_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Keypoint-Graph-Driven_Learning_Framework_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Shaobo and Zhao, Wanqing and Guan, Ziyu and Peng, Xianlin and Peng, Jinye},
title = {Keypoint-Graph-Driven Learning Framework for Object Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Many recent 6D pose estimation methods exploited object 3D models to generate synthetic images for training because labels come for free. However, due to the domain shift of data distributions between real images and synthetic images, the network trained only on synthetic images fails to capture robust features in real... |
Bhat_Deep_Burst_Super-Resolution_CVPR_2021_paper | Deep Burst Super-Resolution | [
"Goutam Bhat",
"Martin Danelljan",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_Deep_Burst_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhat_Deep_Burst_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhat_Deep_Burst_Super-Resolution_CVPR_2021_supplemental.pdf | 2101.10997 | cvf | @InProceedings{Bhat_2021_CVPR,
author = {Bhat, Goutam and Danelljan, Martin and Van Gool, Luc and Timofte, Radu},
title = {Deep Burst Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. In contrast, multi-frame super-resolution (MFSR) offers the possibility of reconstructing rich details by combining signal i... |
Li_Transferable_Semantic_Augmentation_for_Domain_Adaptation_CVPR_2021_paper | Transferable Semantic Augmentation for Domain Adaptation | [
"Shuang Li",
"Mixue Xie",
"Kaixiong Gong",
"Chi Harold Liu",
"Yulin Wang",
"Wei Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Transferable_Semantic_Augmentation_for_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Transferable_Semantic_Augmentation_for_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Transferable_Semantic_Augmentation_CVPR_2021_supplemental.pdf | 2103.12562 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Shuang and Xie, Mixue and Gong, Kaixiong and Liu, Chi Harold and Wang, Yulin and Li, Wei},
title = {Transferable Semantic Augmentation for Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation algorithms attend to adapting feature representations across two domains with the guidance of a shared source-supervised classifier. However,... |
Zheng_Patchwise_Generative_ConvNet_Training_Energy-Based_Models_From_a_Single_Natural_CVPR_2021_paper | Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning | [
"Zilong Zheng",
"Jianwen Xie",
"Ping Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Patchwise_Generative_ConvNet_Training_Energy-Based_Models_From_a_Single_Natural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Patchwise_Generative_ConvNet_Training_Energy-Based_Models_From_a_Single_Natural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Patchwise_Generative_ConvNet_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Zilong and Xie, Jianwen and Li, Ping},
title = {Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the distribution of patches within the image without relying on external training data. Different from prior works that model such distributions implicitly with a top-down late... |
Nguyen_Clusformer_A_Transformer_Based_Clustering_Approach_to_Unsupervised_Large-Scale_Face_CVPR_2021_paper | Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition | [
"Xuan-Bac Nguyen",
"Duc Toan Bui",
"Chi Nhan Duong",
"Tien D. Bui",
"Khoa Luu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Clusformer_A_Transformer_Based_Clustering_Approach_to_Unsupervised_Large-Scale_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Clusformer_A_Transformer_Based_Clustering_Approach_to_Unsupervised_Large-Scale_Face_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Nguyen_2021_CVPR,
author = {Nguyen, Xuan-Bac and Bui, Duc Toan and Duong, Chi Nhan and Bui, Tien D. and Luu, Khoa},
title = {Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition},
booktitle = {Proceedings of the IEEE/CVF C... | The research in automatic unsupervised visual clustering has received considerable attention over the last couple years. It aims at explaining distributions of unlabeled visual images by clustering them via a parameterized model of appearance. Graph Convolutional Neural Networks (GCN) have recently been one of the most... |
Liu_No_Frame_Left_Behind_Full_Video_Action_Recognition_CVPR_2021_paper | No Frame Left Behind: Full Video Action Recognition | [
"Xin Liu",
"Silvia L. Pintea",
"Fatemeh Karimi Nejadasl",
"Olaf Booij",
"Jan C. van Gemert"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_No_Frame_Left_Behind_Full_Video_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_No_Frame_Left_Behind_Full_Video_Action_Recognition_CVPR_2021_paper.pdf | null | 2103.15395 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xin and Pintea, Silvia L. and Nejadasl, Fatemeh Karimi and Booij, Olaf and van Gemert, Jan C.},
title = {No Frame Left Behind: Full Video Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here... |
Tuan_ColorRL_Reinforced_Coloring_for_End-to-End_Instance_Segmentation_CVPR_2021_paper | ColorRL: Reinforced Coloring for End-to-End Instance Segmentation | [
"Tran Anh Tuan",
"Nguyen Tuan Khoa",
"Tran Minh Quan",
"Won-Ki Jeong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tuan_ColorRL_Reinforced_Coloring_for_End-to-End_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tuan_ColorRL_Reinforced_Coloring_for_End-to-End_Instance_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tuan_ColorRL_Reinforced_Coloring_CVPR_2021_supplemental.pdf | 2005.07058 | title_judge | @InProceedings{Tuan_2021_CVPR,
author = {Tuan, Tran Anh and Khoa, Nguyen Tuan and Quan, Tran Minh and Jeong, Won-Ki},
title = {ColorRL: Reinforced Coloring for End-to-End Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision. Although many feed-forward networks produce high-quality binary segmentation on different types of images, their final result heavily relies o... |
Duggal_Compatibility-Aware_Heterogeneous_Visual_Search_CVPR_2021_paper | Compatibility-Aware Heterogeneous Visual Search | [
"Rahul Duggal",
"Hao Zhou",
"Shuo Yang",
"Yuanjun Xiong",
"Wei Xia",
"Zhuowen Tu",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duggal_Compatibility-Aware_Heterogeneous_Visual_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duggal_Compatibility-Aware_Heterogeneous_Visual_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duggal_Compatibility-Aware_Heterogeneous_Visual_CVPR_2021_supplemental.pdf | 2105.06047 | cvf | @InProceedings{Duggal_2021_CVPR,
author = {Duggal, Rahul and Zhou, Hao and Yang, Shuo and Xiong, Yuanjun and Xia, Wei and Tu, Zhuowen and Soatto, Stefano},
title = {Compatibility-Aware Heterogeneous Visual Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery images. Such systems inherently face a hard accuracy-efficiency trade-off: the embedding model needs to be large enough to ensure high accura... |
Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper | WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos | [
"Mingfei Gao",
"Yingbo Zhou",
"Ran Xu",
"Richard Socher",
"Caiming Xiong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper.pdf | null | 2006.03732 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Mingfei and Zhou, Yingbo and Xu, Ran and Socher, Richard and Xiong, Caiming},
title = {WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a we... |
Liu_Deep_Dual_Consecutive_Network_for_Human_Pose_Estimation_CVPR_2021_paper | Deep Dual Consecutive Network for Human Pose Estimation | [
"Zhenguang Liu",
"Haoming Chen",
"Runyang Feng",
"Shuang Wu",
"Shouling Ji",
"Bailin Yang",
"Xun Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Dual_Consecutive_Network_for_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Dual_Consecutive_Network_for_Human_Pose_Estimation_CVPR_2021_paper.pdf | null | 2103.07254 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Zhenguang and Chen, Haoming and Feng, Runyang and Wu, Shuang and Ji, Shouling and Yang, Bailin and Wang, Xun},
title = {Deep Dual Consecutive Network for Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images, their performances come short when we apply these models to video sequences. Prevalent shortcomings include the failure to handle motion bl... |
Li_Uncertainty-Aware_Joint_Salient_Object_and_Camouflaged_Object_Detection_CVPR_2021_paper | Uncertainty-Aware Joint Salient Object and Camouflaged Object Detection | [
"Aixuan Li",
"Jing Zhang",
"Yunqiu Lv",
"Bowen Liu",
"Tong Zhang",
"Yuchao Dai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Uncertainty-Aware_Joint_Salient_Object_and_Camouflaged_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Uncertainty-Aware_Joint_Salient_Object_and_Camouflaged_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Uncertainty-Aware_Joint_Salient_CVPR_2021_supplemental.zip | 2104.02628 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Aixuan and Zhang, Jing and Lv, Yunqiu and Liu, Bowen and Zhang, Tong and Dai, Yuchao},
title = {Uncertainty-Aware Joint Salient Object and Camouflaged Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, we propose a paradigm of leveraging the contradictory informatio... |
Yang_HourNAS_Extremely_Fast_Neural_Architecture_Search_Through_an_Hourglass_Lens_CVPR_2021_paper | HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens | [
"Zhaohui Yang",
"Yunhe Wang",
"Xinghao Chen",
"Jianyuan Guo",
"Wei Zhang",
"Chao Xu",
"Chunjing Xu",
"Dacheng Tao",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_HourNAS_Extremely_Fast_Neural_Architecture_Search_Through_an_Hourglass_Lens_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_HourNAS_Extremely_Fast_Neural_Architecture_Search_Through_an_Hourglass_Lens_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_HourNAS_Extremely_Fast_CVPR_2021_supplemental.pdf | 2005.14446 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Zhaohui and Wang, Yunhe and Chen, Xinghao and Guo, Jianyuan and Zhang, Wei and Xu, Chao and Xu, Chunjing and Tao, Dacheng and Xu, Chang},
title = {HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens},
booktitle = {Proceedings of ... | Neural Architecture Search (NAS) aims to automatically discover optimal architectures. In this paper, we propose an hourglass-inspired approach (HourNAS) for extremely fast NAS. It is motivated by the fact that the effects of the architecture often proceed from the vital few blocks. Acting like the narrow neck of an ho... |
Song_Tree-Like_Decision_Distillation_CVPR_2021_paper | Tree-Like Decision Distillation | [
"Jie Song",
"Haofei Zhang",
"Xinchao Wang",
"Mengqi Xue",
"Ying Chen",
"Li Sun",
"Dacheng Tao",
"Mingli Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Tree-Like_Decision_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Tree-Like_Decision_Distillation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Tree-Like_Decision_Distillation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Song_2021_CVPR,
author = {Song, Jie and Zhang, Haofei and Wang, Xinchao and Xue, Mengqi and Chen, Ying and Sun, Li and Tao, Dacheng and Song, Mingli},
title = {Tree-Like Decision Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model. Prior methods achieve this by making the student imitate shallow behaviors, such as soft targets, features, or attention, of the teacher. In this paper, we argue that what real... |
Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper | GAN Prior Embedded Network for Blind Face Restoration in the Wild | [
"Tao Yang",
"Peiran Ren",
"Xuansong Xie",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_GAN_Prior_Embedded_CVPR_2021_supplemental.pdf | 2105.06070 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Tao and Ren, Peiran and Xie, Xuansong and Zhang, Lei},
title = {GAN Prior Embedded Network for Blind Face Restoration in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) ba... |
Hui_Collaborative_Spatial-Temporal_Modeling_for_Language-Queried_Video_Actor_Segmentation_CVPR_2021_paper | Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation | [
"Tianrui Hui",
"Shaofei Huang",
"Si Liu",
"Zihan Ding",
"Guanbin Li",
"Wenguan Wang",
"Jizhong Han",
"Fei Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hui_Collaborative_Spatial-Temporal_Modeling_for_Language-Queried_Video_Actor_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hui_Collaborative_Spatial-Temporal_Modeling_for_Language-Queried_Video_Actor_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hui_Collaborative_Spatial-Temporal_Modeling_CVPR_2021_supplemental.pdf | 2105.06818 | cvf | @InProceedings{Hui_2021_CVPR,
author = {Hui, Tianrui and Huang, Shaofei and Liu, Si and Ding, Zihan and Li, Guanbin and Wang, Wenguan and Han, Jizhong and Wang, Fei},
title = {Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation},
booktitle = {Proceedings of the IEEE/... | Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. Existing methods adopt 3D CNNs over the video clip as a general encoder to extract a mixed spatio-temporal feature for the target frame. Thou... |
Lin_Drafting_and_Revision_Laplacian_Pyramid_Network_for_Fast_High-Quality_Artistic_CVPR_2021_paper | Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer | [
"Tianwei Lin",
"Zhuoqi Ma",
"Fu Li",
"Dongliang He",
"Xin Li",
"Errui Ding",
"Nannan Wang",
"Jie Li",
"Xinbo Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Drafting_and_Revision_Laplacian_Pyramid_Network_for_Fast_High-Quality_Artistic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Drafting_and_Revision_Laplacian_Pyramid_Network_for_Fast_High-Quality_Artistic_CVPR_2021_paper.pdf | null | 2104.05376 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Tianwei and Ma, Zhuoqi and Li, Fu and He, Dongliang and Li, Xin and Ding, Errui and Wang, Nannan and Li, Jie and Gao, Xinbo},
title = {Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer},
booktitle = {Proceedings... | Artistic style transfer aims at migrating the style from an example image to a content image. Currently, optimization-based methods have achieved great stylization quality, but expensive time cost restricts their practical applications. Meanwhile, feed-forward methods still fail to synthesize complex style, especially ... |
Girish_The_Lottery_Ticket_Hypothesis_for_Object_Recognition_CVPR_2021_paper | The Lottery Ticket Hypothesis for Object Recognition | [
"Sharath Girish",
"Shishira R Maiya",
"Kamal Gupta",
"Hao Chen",
"Larry S. Davis",
"Abhinav Shrivastava"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Girish_The_Lottery_Ticket_Hypothesis_for_Object_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Girish_The_Lottery_Ticket_Hypothesis_for_Object_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Girish_The_Lottery_Ticket_CVPR_2021_supplemental.pdf | 2012.04643 | cvf | @InProceedings{Girish_2021_CVPR,
author = {Girish, Sharath and Maiya, Shishira R and Gupta, Kamal and Chen, Hao and Davis, Larry S. and Shrivastava, Abhinav},
title = {The Lottery Ticket Hypothesis for Object Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep networks that are computationally expensive and have huge memory footprints. This makes it exceedingly difficult to deploy these systems on low pow... |
Liu_Refer-It-in-RGBD_A_Bottom-Up_Approach_for_3D_Visual_Grounding_in_RGBD_CVPR_2021_paper | Refer-It-in-RGBD: A Bottom-Up Approach for 3D Visual Grounding in RGBD Images | [
"Haolin Liu",
"Anran Lin",
"Xiaoguang Han",
"Lei Yang",
"Yizhou Yu",
"Shuguang Cui"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Refer-It-in-RGBD_A_Bottom-Up_Approach_for_3D_Visual_Grounding_in_RGBD_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Refer-It-in-RGBD_A_Bottom-Up_Approach_for_3D_Visual_Grounding_in_RGBD_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Refer-It-in-RGBD_A_Bottom-Up_CVPR_2021_supplemental.pdf | 2103.07894 | title_snapshot | @InProceedings{Liu_2021_CVPR,
author = {Liu, Haolin and Lin, Anran and Han, Xiaoguang and Yang, Lei and Yu, Yizhou and Cui, Shuguang},
title = {Refer-It-in-RGBD: A Bottom-Up Approach for 3D Visual Grounding in RGBD Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion. In contrast to previous works that directly generate object proposals for grounding in the 3D s... |
Achille_LQF_Linear_Quadratic_Fine-Tuning_CVPR_2021_paper | LQF: Linear Quadratic Fine-Tuning | [
"Alessandro Achille",
"Aditya Golatkar",
"Avinash Ravichandran",
"Marzia Polito",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Achille_LQF_Linear_Quadratic_Fine-Tuning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Achille_LQF_Linear_Quadratic_Fine-Tuning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Achille_LQF_Linear_Quadratic_CVPR_2021_supplemental.pdf | 2012.11140 | cvf | @InProceedings{Achille_2021_CVPR,
author = {Achille, Alessandro and Golatkar, Aditya and Ravichandran, Avinash and Polito, Marzia and Soatto, Stefano},
title = {LQF: Linear Quadratic Fine-Tuning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tunin... |
Liu_Watching_You_Global-Guided_Reciprocal_Learning_for_Video-Based_Person_Re-Identification_CVPR_2021_paper | Watching You: Global-Guided Reciprocal Learning for Video-Based Person Re-Identification | [
"Xuehu Liu",
"Pingping Zhang",
"Chenyang Yu",
"Huchuan Lu",
"Xiaoyun Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Watching_You_Global-Guided_Reciprocal_Learning_for_Video-Based_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Watching_You_Global-Guided_Reciprocal_Learning_for_Video-Based_Person_Re-Identification_CVPR_2021_paper.pdf | null | 2103.04337 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xuehu and Zhang, Pingping and Yu, Chenyang and Lu, Huchuan and Yang, Xiaoyun},
title = {Watching You: Global-Guided Reciprocal Learning for Video-Based Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Video-based person re-identification (Re-ID) aims to automatically retrieve video sequences of the same person under non-overlapping cameras. To achieve this goal, it is the key to fully utilize abundant spatial and temporal cues in videos. Existing methods usually focus on the most conspicuous image regions, thus they... |
Huang_S3_Learnable_Sparse_Signal_Superdensity_for_Guided_Depth_Estimation_CVPR_2021_paper | S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation | [
"Yu-Kai Huang",
"Yueh-Cheng Liu",
"Tsung-Han Wu",
"Hung-Ting Su",
"Yu-Cheng Chang",
"Tsung-Lin Tsou",
"Yu-An Wang",
"Winston H. Hsu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_S3_Learnable_Sparse_Signal_Superdensity_for_Guided_Depth_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_S3_Learnable_Sparse_Signal_Superdensity_for_Guided_Depth_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_S3_Learnable_Sparse_CVPR_2021_supplemental.pdf | 2103.02396 | title_judge | @InProceedings{Huang_2021_CVPR,
author = {Huang, Yu-Kai and Liu, Yueh-Cheng and Wu, Tsung-Han and Su, Hung-Ting and Chang, Yu-Cheng and Tsou, Tsung-Lin and Wang, Yu-An and Hsu, Winston H.},
title = {S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation},
booktitle = {Proceedings of the... | Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been leveraged as guidance for enhancing dense depth estimation, the improvement is limited due to its low density and imbalanced distribution. To ... |
Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper | Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking | [
"Ning Wang",
"Wengang Zhou",
"Jie Wang",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.pdf | null | 2103.11681 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Ning and Zhou, Wengang and Wang, Jie and Li, Houqiang},
title = {Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. In this work, we bridge the individual video frames and explore the temporal contexts across them via a transformer architecture for robust object tracking. Different from class... |
Kappel_High-Fidelity_Neural_Human_Motion_Transfer_From_Monocular_Video_CVPR_2021_paper | High-Fidelity Neural Human Motion Transfer From Monocular Video | [
"Moritz Kappel",
"Vladislav Golyanik",
"Mohamed Elgharib",
"Jann-Ole Henningson",
"Hans-Peter Seidel",
"Susana Castillo",
"Christian Theobalt",
"Marcus Magnor"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kappel_High-Fidelity_Neural_Human_Motion_Transfer_From_Monocular_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kappel_High-Fidelity_Neural_Human_Motion_Transfer_From_Monocular_Video_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kappel_High-Fidelity_Neural_Human_CVPR_2021_supplemental.zip | 2012.10974 | cvf | @InProceedings{Kappel_2021_CVPR,
author = {Kappel, Moritz and Golyanik, Vladislav and Elgharib, Mohamed and Henningson, Jann-Ole and Seidel, Hans-Peter and Castillo, Susana and Theobalt, Christian and Magnor, Marcus},
title = {High-Fidelity Neural Human Motion Transfer From Monocular Video},
booktitl... | Video-based human motion transfer creates video animations of humans following a source motion. Current methods show remarkable results for tightly-clad subjects. However, the lack of temporally consistent handling of plausible clothing dynamics, including fine and high-frequency details, significantly limits the attai... |
Girard_Polygonal_Building_Extraction_by_Frame_Field_Learning_CVPR_2021_paper | Polygonal Building Extraction by Frame Field Learning | [
"Nicolas Girard",
"Dmitriy Smirnov",
"Justin Solomon",
"Yuliya Tarabalka"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Girard_Polygonal_Building_Extraction_by_Frame_Field_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Girard_Polygonal_Building_Extraction_by_Frame_Field_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Girard_Polygonal_Building_Extraction_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Girard_2021_CVPR,
author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},
title = {Polygonal Building Extraction by Frame Field Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation m... |
Weder_NeuralFusion_Online_Depth_Fusion_in_Latent_Space_CVPR_2021_paper | NeuralFusion: Online Depth Fusion in Latent Space | [
"Silvan Weder",
"Johannes L. Schonberger",
"Marc Pollefeys",
"Martin R. Oswald"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Weder_NeuralFusion_Online_Depth_Fusion_in_Latent_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Weder_NeuralFusion_Online_Depth_Fusion_in_Latent_Space_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Weder_NeuralFusion_Online_Depth_CVPR_2021_supplemental.pdf | 2011.14791 | cvf | @InProceedings{Weder_2021_CVPR,
author = {Weder, Silvan and Schonberger, Johannes L. and Pollefeys, Marc and Oswald, Martin R.},
title = {NeuralFusion: Online Depth Fusion in Latent Space},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the ... |
Gong_PoseAug_A_Differentiable_Pose_Augmentation_Framework_for_3D_Human_Pose_CVPR_2021_paper | PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation | [
"Kehong Gong",
"Jianfeng Zhang",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_PoseAug_A_Differentiable_Pose_Augmentation_Framework_for_3D_Human_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_PoseAug_A_Differentiable_Pose_Augmentation_Framework_for_3D_Human_Pose_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_PoseAug_A_Differentiable_CVPR_2021_supplemental.pdf | 2105.02465 | cvf | @InProceedings{Gong_2021_CVPR,
author = {Gong, Kehong and Zhang, Jianfeng and Feng, Jiashi},
title = {PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater di... |
Imran_Depth_Completion_With_Twin_Surface_Extrapolation_at_Occlusion_Boundaries_CVPR_2021_paper | Depth Completion With Twin Surface Extrapolation at Occlusion Boundaries | [
"Saif Imran",
"Xiaoming Liu",
"Daniel Morris"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Imran_Depth_Completion_With_Twin_Surface_Extrapolation_at_Occlusion_Boundaries_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Imran_Depth_Completion_With_Twin_Surface_Extrapolation_at_Occlusion_Boundaries_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Imran_Depth_Completion_With_CVPR_2021_supplemental.pdf | 2104.02253 | cvf | @InProceedings{Imran_2021_CVPR,
author = {Imran, Saif and Liu, Xiaoming and Morris, Daniel},
title = {Depth Completion With Twin Surface Extrapolation at Occlusion Boundaries},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion b... |
Zhu_Learning_the_Superpixel_in_a_Non-Iterative_and_Lifelong_Manner_CVPR_2021_paper | Learning the Superpixel in a Non-Iterative and Lifelong Manner | [
"Lei Zhu",
"Qi She",
"Bin Zhang",
"Yanye Lu",
"Zhilin Lu",
"Duo Li",
"Jie Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_the_Superpixel_in_a_Non-Iterative_and_Lifelong_Manner_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Learning_the_Superpixel_in_a_Non-Iterative_and_Lifelong_Manner_CVPR_2021_paper.pdf | null | 2103.10681 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Lei and She, Qi and Zhang, Bin and Lu, Yanye and Lu, Zhilin and Li, Duo and Hu, Jie},
title = {Learning the Superpixel in a Non-Iterative and Lifelong Manner},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design ... |
Anokhin_Image_Generators_With_Conditionally-Independent_Pixel_Synthesis_CVPR_2021_paper | Image Generators With Conditionally-Independent Pixel Synthesis | [
"Ivan Anokhin",
"Kirill Demochkin",
"Taras Khakhulin",
"Gleb Sterkin",
"Victor Lempitsky",
"Denis Korzhenkov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Anokhin_Image_Generators_With_Conditionally-Independent_Pixel_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Anokhin_Image_Generators_With_Conditionally-Independent_Pixel_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Anokhin_Image_Generators_With_CVPR_2021_supplemental.pdf | 2011.13775 | cvf | @InProceedings{Anokhin_2021_CVPR,
author = {Anokhin, Ivan and Demochkin, Kirill and Khakhulin, Taras and Sterkin, Gleb and Lempitsky, Victor and Korzhenkov, Denis},
title = {Image Generators With Conditionally-Independent Pixel Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. Here, we present a new architecture for image generators, where the color value at each pixel is computed independently given the value of a r... |
Liao_Towards_Good_Practices_for_Efficiently_Annotating_Large-Scale_Image_Classification_Datasets_CVPR_2021_paper | Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets | [
"Yuan-Hong Liao",
"Amlan Kar",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liao_Towards_Good_Practices_for_Efficiently_Annotating_Large-Scale_Image_Classification_Datasets_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liao_Towards_Good_Practices_for_Efficiently_Annotating_Large-Scale_Image_Classification_Datasets_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liao_Towards_Good_Practices_CVPR_2021_supplemental.pdf | 2104.12690 | cvf | @InProceedings{Liao_2021_CVPR,
author = {Liao, Yuan-Hong and Kar, Amlan and Fidler, Sanja},
title = {Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of i... |
Wang_Seesaw_Loss_for_Long-Tailed_Instance_Segmentation_CVPR_2021_paper | Seesaw Loss for Long-Tailed Instance Segmentation | [
"Jiaqi Wang",
"Wenwei Zhang",
"Yuhang Zang",
"Yuhang Cao",
"Jiangmiao Pang",
"Tao Gong",
"Kai Chen",
"Ziwei Liu",
"Chen Change Loy",
"Dahua Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Seesaw_Loss_for_Long-Tailed_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Seesaw_Loss_for_Long-Tailed_Instance_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Seesaw_Loss_for_CVPR_2021_supplemental.pdf | 2008.10032 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jiaqi and Zhang, Wenwei and Zang, Yuhang and Cao, Yuhang and Pang, Jiangmiao and Gong, Tao and Chen, Kai and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
title = {Seesaw Loss for Long-Tailed Instance Segmentation},
booktitle = {Proceedings of the IEE... | Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative sa... |
Gafni_Dynamic_Neural_Radiance_Fields_for_Monocular_4D_Facial_Avatar_Reconstruction_CVPR_2021_paper | Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction | [
"Guy Gafni",
"Justus Thies",
"Michael Zollhofer",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gafni_Dynamic_Neural_Radiance_Fields_for_Monocular_4D_Facial_Avatar_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gafni_Dynamic_Neural_Radiance_Fields_for_Monocular_4D_Facial_Avatar_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gafni_Dynamic_Neural_Radiance_CVPR_2021_supplemental.zip | 2012.03065 | cvf | @InProceedings{Gafni_2021_CVPR,
author = {Gafni, Guy and Thies, Justus and Zollhofer, Michael and Niessner, Matthias},
title = {Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including nove... |
Qian_PU-GCN_Point_Cloud_Upsampling_Using_Graph_Convolutional_Networks_CVPR_2021_paper | PU-GCN: Point Cloud Upsampling Using Graph Convolutional Networks | [
"Guocheng Qian",
"Abdulellah Abualshour",
"Guohao Li",
"Ali Thabet",
"Bernard Ghanem"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_PU-GCN_Point_Cloud_Upsampling_Using_Graph_Convolutional_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_PU-GCN_Point_Cloud_Upsampling_Using_Graph_Convolutional_Networks_CVPR_2021_paper.pdf | null | 1912.03264 | title_snapshot | @InProceedings{Qian_2021_CVPR,
author = {Qian, Guocheng and Abualshour, Abdulellah and Li, Guohao and Thabet, Ali and Ghanem, Bernard},
title = {PU-GCN: Point Cloud Upsampling Using Graph Convolutional Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from po... |
Cordonnier_Differentiable_Patch_Selection_for_Image_Recognition_CVPR_2021_paper | Differentiable Patch Selection for Image Recognition | [
"Jean-Baptiste Cordonnier",
"Aravindh Mahendran",
"Alexey Dosovitskiy",
"Dirk Weissenborn",
"Jakob Uszkoreit",
"Thomas Unterthiner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cordonnier_Differentiable_Patch_Selection_for_Image_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cordonnier_Differentiable_Patch_Selection_for_Image_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cordonnier_Differentiable_Patch_Selection_CVPR_2021_supplemental.pdf | 2104.03059 | cvf | @InProceedings{Cordonnier_2021_CVPR,
author = {Cordonnier, Jean-Baptiste and Mahendran, Aravindh and Dosovitskiy, Alexey and Weissenborn, Dirk and Uszkoreit, Jakob and Unterthiner, Thomas},
title = {Differentiable Patch Selection for Image Recognition},
booktitle = {Proceedings of the IEEE/CVF Confer... | Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high re... |
Wang_MaX-DeepLab_End-to-End_Panoptic_Segmentation_With_Mask_Transformers_CVPR_2021_paper | MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers | [
"Huiyu Wang",
"Yukun Zhu",
"Hartwig Adam",
"Alan Yuille",
"Liang-Chieh Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_MaX-DeepLab_End-to-End_Panoptic_Segmentation_With_Mask_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_MaX-DeepLab_End-to-End_Panoptic_Segmentation_With_Mask_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_MaX-DeepLab_End-to-End_Panoptic_CVPR_2021_supplemental.pdf | 2012.00759 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Huiyu and Zhu, Yukun and Adam, Hartwig and Yuille, Alan and Chen, Liang-Chieh},
title = {MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detection, non-maximum suppression, thing-stuff merging, etc. Although these sub-tasks are tackled by area expe... |
Xiao_Improving_Transferability_of_Adversarial_Patches_on_Face_Recognition_With_Generative_CVPR_2021_paper | Improving Transferability of Adversarial Patches on Face Recognition With Generative Models | [
"Zihao Xiao",
"Xianfeng Gao",
"Chilin Fu",
"Yinpeng Dong",
"Wei Gao",
"Xiaolu Zhang",
"Jun Zhou",
"Jun Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_Improving_Transferability_of_Adversarial_Patches_on_Face_Recognition_With_Generative_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Improving_Transferability_of_Adversarial_Patches_on_Face_Recognition_With_Generative_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_Improving_Transferability_of_CVPR_2021_supplemental.pdf | 2106.15058 | title_snapshot | @InProceedings{Xiao_2021_CVPR,
author = {Xiao, Zihao and Gao, Xianfeng and Fu, Chilin and Dong, Yinpeng and Gao, Wei and Zhang, Xiaolu and Zhou, Jun and Zhu, Jun},
title = {Improving Transferability of Adversarial Patches on Face Recognition With Generative Models},
booktitle = {Proceedings of the IE... | Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security sensitive applications. However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new se... |
Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper | Counterfactual VQA: A Cause-Effect Look at Language Bias | [
"Yulei Niu",
"Kaihua Tang",
"Hanwang Zhang",
"Zhiwu Lu",
"Xian-Sheng Hua",
"Ji-Rong Wen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Niu_Counterfactual_VQA_A_Cause-Effect_Look_at_Language_Bias_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Niu_Counterfactual_VQA_A_CVPR_2021_supplemental.pdf | 2006.04315 | cvf | @InProceedings{Niu_2021_CVPR,
author = {Niu, Yulei and Tang, Kaihua and Zhang, Hanwang and Lu, Zhiwu and Hua, Xian-Sheng and Wen, Ji-Rong},
title = {Counterfactual VQA: A Cause-Effect Look at Language Bias},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Recent VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. In this paper, we investigate how to capture and mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framewo... |
Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper | Denoise and Contrast for Category Agnostic Shape Completion | [
"Antonio Alliegro",
"Diego Valsesia",
"Giulia Fracastoro",
"Enrico Magli",
"Tatiana Tommasi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Alliegro_Denoise_and_Contrast_CVPR_2021_supplemental.pdf | 2103.16671 | cvf | @InProceedings{Alliegro_2021_CVPR,
author = {Alliegro, Antonio and Valsesia, Diego and Fracastoro, Giulia and Magli, Enrico and Tommasi, Tatiana},
title = {Denoise and Contrast for Category Agnostic Shape Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region around it. Local and global information are encoded in a combined embedding. A denoising pretext task provides the network with the needed lo... |
Li_Transformation_Invariant_Few-Shot_Object_Detection_CVPR_2021_paper | Transformation Invariant Few-Shot Object Detection | [
"Aoxue Li",
"Zhenguo Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Transformation_Invariant_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Transformation_Invariant_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Transformation_Invariant_Few-Shot_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Aoxue and Li, Zhenguo},
title = {Transformation Invariant Few-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {309... | Few-shot object detection (FSOD) aims to learn detectors that can be generalized to novel classes with only a few instances. Unlike previous attempts that exploit meta-learning techniques to facilitate FSOD, this work tackles the problem from the perspective of sample expansion. To this end, we propose a simple yet eff... |
Li_2D_or_not_2D_Adaptive_3D_Convolution_Selection_for_Efficient_CVPR_2021_paper | 2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition | [
"Hengduo Li",
"Zuxuan Wu",
"Abhinav Shrivastava",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_2D_or_not_2D_Adaptive_3D_Convolution_Selection_for_Efficient_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_2D_or_not_2D_Adaptive_3D_Convolution_Selection_for_Efficient_CVPR_2021_paper.pdf | null | 2012.14950 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Hengduo and Wu, Zuxuan and Shrivastava, Abhinav and Davis, Larry S.},
title = {2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | 3D convolutional networks are prevalent for video recognition. While achieving excellent recognition performance on standard benchmarks, they operate on a sequence of frames with 3D convolutions and thus are computationally demanding. Exploiting large variations among different videos, we introduce Ada3D, a conditional... |
Zhang_Temporal_Query_Networks_for_Fine-Grained_Video_Understanding_CVPR_2021_paper | Temporal Query Networks for Fine-Grained Video Understanding | [
"Chuhan Zhang",
"Ankush Gupta",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Temporal_Query_Networks_for_Fine-Grained_Video_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Temporal_Query_Networks_for_Fine-Grained_Video_Understanding_CVPR_2021_paper.pdf | null | 2104.09496 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Chuhan and Gupta, Ankush and Zisserman, Andrew},
title = {Temporal Query Networks for Fine-Grained Video Understanding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. We cast this into a query-response mechanism, where each query addresses a particular question, and has its own response label set. We make t... |
Skorokhodov_Adversarial_Generation_of_Continuous_Images_CVPR_2021_paper | Adversarial Generation of Continuous Images | [
"Ivan Skorokhodov",
"Savva Ignatyev",
"Mohamed Elhoseiny"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Skorokhodov_Adversarial_Generation_of_Continuous_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Skorokhodov_Adversarial_Generation_of_Continuous_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Skorokhodov_Adversarial_Generation_of_CVPR_2021_supplemental.pdf | 2011.12026 | cvf | @InProceedings{Skorokhodov_2021_CVPR,
author = {Skorokhodov, Ivan and Ignatyev, Savva and Elhoseiny, Mohamed},
title = {Adversarial Generation of Continuous Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) -- an MLP that predicts an RGB pixel value given its (x,y) coordinate. In this paper, we propose two novel architectura... |
Khandelwal_UniT_Unified_Knowledge_Transfer_for_Any-Shot_Object_Detection_and_Segmentation_CVPR_2021_paper | UniT: Unified Knowledge Transfer for Any-Shot Object Detection and Segmentation | [
"Siddhesh Khandelwal",
"Raghav Goyal",
"Leonid Sigal"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Khandelwal_UniT_Unified_Knowledge_Transfer_for_Any-Shot_Object_Detection_and_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Khandelwal_UniT_Unified_Knowledge_Transfer_for_Any-Shot_Object_Detection_and_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Khandelwal_UniT_Unified_Knowledge_CVPR_2021_supplemental.pdf | 2006.07502 | cvf | @InProceedings{Khandelwal_2021_CVPR,
author = {Khandelwal, Siddhesh and Goyal, Raghav and Sigal, Leonid},
title = {UniT: Unified Knowledge Transfer for Any-Shot Object Detection and Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate this look at varying degrees and quality of supervision. Weakly-supervised approaches draw on image-level labels to build detectors/segmentor... |
Sun_Indoor_Panorama_Planar_3D_Reconstruction_via_Divide_and_Conquer_CVPR_2021_paper | Indoor Panorama Planar 3D Reconstruction via Divide and Conquer | [
"Cheng Sun",
"Chi-Wei Hsiao",
"Ning-Hsu Wang",
"Min Sun",
"Hwann-Tzong Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Indoor_Panorama_Planar_3D_Reconstruction_via_Divide_and_Conquer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Indoor_Panorama_Planar_3D_Reconstruction_via_Divide_and_Conquer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Indoor_Panorama_Planar_CVPR_2021_supplemental.pdf | 2106.14166 | title_snapshot | @InProceedings{Sun_2021_CVPR,
author = {Sun, Cheng and Hsiao, Chi-Wei and Wang, Ning-Hsu and Sun, Min and Chen, Hwann-Tzong},
title = {Indoor Panorama Planar 3D Reconstruction via Divide and Conquer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Indoor panorama typically consists of human-made structures parallel or perpendicular to gravity. We leverage this phenomenon to approximate the scene in a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this end, we propose an effective divide-and-conquer strategy that divides pixels based on their... |
Wu_Embedded_Discriminative_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper | Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation | [
"Tong Wu",
"Junshi Huang",
"Guangyu Gao",
"Xiaoming Wei",
"Xiaolin Wei",
"Xuan Luo",
"Chi Harold Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Embedded_Discriminative_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Embedded_Discriminative_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wu_2021_CVPR,
author = {Wu, Tong and Huang, Junshi and Gao, Guangyu and Wei, Xiaoming and Wei, Xiaolin and Luo, Xuan and Liu, Chi Harold},
title = {Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Confere... | Weakly Supervised Semantic Segmentation (WSSS) with image-level annotation uses class activation maps from the classifier as pseudo-labels for semantic segmentation. However, such activation maps usually highlight the local discriminative regions rather than the whole object, which deviates from the requirement of sema... |
Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper | TextOCR: Towards Large-Scale End-to-End Reasoning for Arbitrary-Shaped Scene Text | [
"Amanpreet Singh",
"Guan Pang",
"Mandy Toh",
"Jing Huang",
"Wojciech Galuba",
"Tal Hassner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_TextOCR_Towards_Large-Scale_CVPR_2021_supplemental.pdf | 2105.05486 | cvf | @InProceedings{Singh_2021_CVPR,
author = {Singh, Amanpreet and Pang, Guan and Toh, Mandy and Huang, Jing and Galuba, Wojciech and Hassner, Tal},
title = {TextOCR: Towards Large-Scale End-to-End Reasoning for Arbitrary-Shaped Scene Text},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. The current systems are crippled by the unavailability of ground truth text annotations for these datasets a... |
Zhang_Distractor-Aware_Fast_Tracking_via_Dynamic_Convolutions_and_MOT_Philosophy_CVPR_2021_paper | Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy | [
"Zikai Zhang",
"Bineng Zhong",
"Shengping Zhang",
"Zhenjun Tang",
"Xin Liu",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Distractor-Aware_Fast_Tracking_via_Dynamic_Convolutions_and_MOT_Philosophy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Distractor-Aware_Fast_Tracking_via_Dynamic_Convolutions_and_MOT_Philosophy_CVPR_2021_paper.pdf | null | 2104.12041 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Zikai and Zhong, Bineng and Zhang, Shengping and Tang, Zhenjun and Liu, Xin and Zhang, Zhaoxiang},
title = {Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | A practical long-term tracker typically contains three key properties, i.e., an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism. However, most state-of-the-art long-term trackers (e.g., Pseudo and re-detecting based ones) do not take all three key properties... |
Vaswani_Scaling_Local_Self-Attention_for_Parameter_Efficient_Visual_Backbones_CVPR_2021_paper | Scaling Local Self-Attention for Parameter Efficient Visual Backbones | [
"Ashish Vaswani",
"Prajit Ramachandran",
"Aravind Srinivas",
"Niki Parmar",
"Blake Hechtman",
"Jonathon Shlens"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Vaswani_Scaling_Local_Self-Attention_for_Parameter_Efficient_Visual_Backbones_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Vaswani_Scaling_Local_Self-Attention_for_Parameter_Efficient_Visual_Backbones_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vaswani_Scaling_Local_Self-Attention_CVPR_2021_supplemental.pdf | 2103.12731 | cvf | @InProceedings{Vaswani_2021_CVPR,
author = {Vaswani, Ashish and Ramachandran, Prajit and Srinivas, Aravind and Parmar, Niki and Hechtman, Blake and Shlens, Jonathon},
title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encoura... |
Liao_Image_Inpainting_Guided_by_Coherence_Priors_of_Semantics_and_Textures_CVPR_2021_paper | Image Inpainting Guided by Coherence Priors of Semantics and Textures | [
"Liang Liao",
"Jing Xiao",
"Zheng Wang",
"Chia-Wen Lin",
"Shin'ichi Satoh"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liao_Image_Inpainting_Guided_by_Coherence_Priors_of_Semantics_and_Textures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liao_Image_Inpainting_Guided_by_Coherence_Priors_of_Semantics_and_Textures_CVPR_2021_paper.pdf | null | 2012.08054 | cvf | @InProceedings{Liao_2021_CVPR,
author = {Liao, Liang and Xiao, Jing and Wang, Zheng and Lin, Chia-Wen and Satoh, Shin'ichi},
title = {Image Inpainting Guided by Coherence Priors of Semantics and Textures},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories remains challenging due to the obscure semantic boundaries and the mixture of different semantic textures. In this paper, we introduce coherence... |
He_Multi-Source_Domain_Adaptation_With_Collaborative_Learning_for_Semantic_Segmentation_CVPR_2021_paper | Multi-Source Domain Adaptation With Collaborative Learning for Semantic Segmentation | [
"Jianzhong He",
"Xu Jia",
"Shuaijun Chen",
"Jianzhuang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_Multi-Source_Domain_Adaptation_With_Collaborative_Learning_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Multi-Source_Domain_Adaptation_With_Collaborative_Learning_for_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Multi-Source_Domain_Adaptation_CVPR_2021_supplemental.pdf | 2103.04717 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Jianzhong and Jia, Xu and Chen, Shuaijun and Liu, Jianzhuang},
title = {Multi-Source Domain Adaptation With Collaborative Learning for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Multi-source unsupervised domain adaptation (MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Firstly, a simple image translation m... |
Yan_Positive-Congruent_Training_Towards_Regression-Free_Model_Updates_CVPR_2021_paper | Positive-Congruent Training: Towards Regression-Free Model Updates | [
"Sijie Yan",
"Yuanjun Xiong",
"Kaustav Kundu",
"Shuo Yang",
"Siqi Deng",
"Meng Wang",
"Wei Xia",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Positive-Congruent_Training_Towards_Regression-Free_Model_Updates_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Positive-Congruent_Training_Towards_Regression-Free_Model_Updates_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Positive-Congruent_Training_Towards_CVPR_2021_supplemental.pdf | 2011.09161 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Sijie and Xiong, Yuanjun and Kundu, Kaustav and Yang, Shuo and Deng, Siqi and Wang, Meng and Xia, Wei and Soatto, Stefano},
title = {Positive-Congruent Training: Towards Regression-Free Model Updates},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error. In image classification, sample-wise inconsistencies appear as "negative flips": A new model incorrectly predicts the output for a test sample that was correctly classified by th... |
Ghodrati_FrameExit_Conditional_Early_Exiting_for_Efficient_Video_Recognition_CVPR_2021_paper | FrameExit: Conditional Early Exiting for Efficient Video Recognition | [
"Amir Ghodrati",
"Babak Ehteshami Bejnordi",
"Amirhossein Habibian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ghodrati_FrameExit_Conditional_Early_Exiting_for_Efficient_Video_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ghodrati_FrameExit_Conditional_Early_Exiting_for_Efficient_Video_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ghodrati_FrameExit_Conditional_Early_CVPR_2021_supplemental.pdf | 2104.13400 | cvf | @InProceedings{Ghodrati_2021_CVPR,
author = {Ghodrati, Amir and Bejnordi, Babak Ehteshami and Habibian, Amirhossein},
title = {FrameExit: Conditional Early Exiting for Efficient Video Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | In this paper, we propose a conditional early exiting framework for efficient video recognition. While existing works focus on selecting a subset of salient frames to reduce the computation costs, we propose to use a simple sampling strategy combined with conditional early exiting to enable efficient recognition. Our m... |
Huang_Neighbor2Neighbor_Self-Supervised_Denoising_From_Single_Noisy_Images_CVPR_2021_paper | Neighbor2Neighbor: Self-Supervised Denoising From Single Noisy Images | [
"Tao Huang",
"Songjiang Li",
"Xu Jia",
"Huchuan Lu",
"Jianzhuang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Neighbor2Neighbor_Self-Supervised_Denoising_From_Single_Noisy_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Neighbor2Neighbor_Self-Supervised_Denoising_From_Single_Noisy_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Neighbor2Neighbor_Self-Supervised_Denoising_CVPR_2021_supplemental.pdf | 2101.02824 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Tao and Li, Songjiang and Jia, Xu and Lu, Huchuan and Liu, Jianzhuang},
title = {Neighbor2Neighbor: Self-Supervised Denoising From Single Noisy Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for supervision limits the wide use of these models. Although there have been a few attempts in training an image denoising model with only single... |
Zhou_Differentiable_Multi-Granularity_Human_Representation_Learning_for_Instance-Aware_Human_Semantic_Parsing_CVPR_2021_paper | Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing | [
"Tianfei Zhou",
"Wenguan Wang",
"Si Liu",
"Yi Yang",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Differentiable_Multi-Granularity_Human_Representation_Learning_for_Instance-Aware_Human_Semantic_Parsing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Differentiable_Multi-Granularity_Human_Representation_Learning_for_Instance-Aware_Human_Semantic_Parsing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Differentiable_Multi-Granularity_Human_CVPR_2021_supplemental.pdf | 2103.04570 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Tianfei and Wang, Wenguan and Liu, Si and Yang, Yi and Van Gool, Luc},
title = {Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information ov... |
Xiao_Dynamic_Weighted_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | Dynamic Weighted Learning for Unsupervised Domain Adaptation | [
"Ni Xiao",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_Dynamic_Weighted_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Dynamic_Weighted_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | null | 2103.13814 | cvf | @InProceedings{Xiao_2021_CVPR,
author = {Xiao, Ni and Zhang, Lei},
title = {Dynamic Weighted Learning for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | Unsupervised domain adaptation (UDA) aims to improve the classification performance on an unlabeled target domain by leveraging information from a fully labeled source domain. Recent approaches explore domain-invariant and class-discriminant representations to tackle this task. These methods, however, ignore the intera... |
Stojanov_Using_Shape_To_Categorize_Low-Shot_Learning_With_an_Explicit_Shape_CVPR_2021_paper | Using Shape To Categorize: Low-Shot Learning With an Explicit Shape Bias | [
"Stefan Stojanov",
"Anh Thai",
"James M. Rehg"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Stojanov_Using_Shape_To_Categorize_Low-Shot_Learning_With_an_Explicit_Shape_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Stojanov_Using_Shape_To_Categorize_Low-Shot_Learning_With_an_Explicit_Shape_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Stojanov_Using_Shape_To_CVPR_2021_supplemental.pdf | 2101.07296 | cvf | @InProceedings{Stojanov_2021_CVPR,
author = {Stojanov, Stefan and Thai, Anh and Rehg, James M.},
title = {Using Shape To Categorize: Low-Shot Learning With an Explicit Shape Bias},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | It is widely accepted that reasoning about object shape is important for object recognition. However, the most powerful object recognition methods today do not explicitly make use of object shape during learning. In this work, motivated by recent developments in low-shot learning, findings in developmental psychology, ... |
Zhou_Face_Forensics_in_the_Wild_CVPR_2021_paper | Face Forensics in the Wild | [
"Tianfei Zhou",
"Wenguan Wang",
"Zhiyuan Liang",
"Jianbing Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Face_Forensics_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Face_Forensics_in_the_Wild_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Face_Forensics_in_CVPR_2021_supplemental.pdf | 2103.16076 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Tianfei and Wang, Wenguan and Liang, Zhiyuan and Shen, Jianbing},
title = {Face Forensics in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}... | On existing public benchmarks, face forgery detection techniques have achieved great success. However, when used in multi-person videos, which often contain many people active in the scene with only a small subset having been manipulated, their performance remains far from being satisfactory. To take face forgery detec... |
Liu_Spatial-Phase_Shallow_Learning_Rethinking_Face_Forgery_Detection_in_Frequency_Domain_CVPR_2021_paper | Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain | [
"Honggu Liu",
"Xiaodan Li",
"Wenbo Zhou",
"Yuefeng Chen",
"Yuan He",
"Hui Xue",
"Weiming Zhang",
"Nenghai Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Spatial-Phase_Shallow_Learning_Rethinking_Face_Forgery_Detection_in_Frequency_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Spatial-Phase_Shallow_Learning_Rethinking_Face_Forgery_Detection_in_Frequency_Domain_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Spatial-Phase_Shallow_Learning_CVPR_2021_supplemental.pdf | 2103.01856 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Honggu and Li, Xiaodan and Zhou, Wenbo and Chen, Yuefeng and He, Yuan and Xue, Hui and Zhang, Weiming and Yu, Nenghai},
title = {Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain},
booktitle = {Proceedings of the IEEE/CV... | The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in obvious changes in the frequency domain, especially in the phase s... |
Chandrasegaran_A_Closer_Look_at_Fourier_Spectrum_Discrepancies_for_CNN-Generated_Images_CVPR_2021_paper | A Closer Look at Fourier Spectrum Discrepancies for CNN-Generated Images Detection | [
"Keshigeyan Chandrasegaran",
"Ngoc-Trung Tran",
"Ngai-Man Cheung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chandrasegaran_A_Closer_Look_at_Fourier_Spectrum_Discrepancies_for_CNN-Generated_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chandrasegaran_A_Closer_Look_at_Fourier_Spectrum_Discrepancies_for_CNN-Generated_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chandrasegaran_A_Closer_Look_CVPR_2021_supplemental.pdf | 2103.17195 | cvf | @InProceedings{Chandrasegaran_2021_CVPR,
author = {Chandrasegaran, Keshigeyan and Tran, Ngoc-Trung and Cheung, Ngai-Man},
title = {A Closer Look at Fourier Spectrum Discrepancies for CNN-Generated Images Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | CNN-based generative modelling has evolved to produce synthetic images indistinguishable from real images in the RGB pixel space. Recent works have observed that CNN-generated images share a systematic shortcoming in replicating high frequency Fourier spectrum decay attributes. Furthermore, these works have successfull... |
Rakotosaona_Learning_Delaunay_Surface_Elements_for_Mesh_Reconstruction_CVPR_2021_paper | Learning Delaunay Surface Elements for Mesh Reconstruction | [
"Marie-Julie Rakotosaona",
"Paul Guerrero",
"Noam Aigerman",
"Niloy J. Mitra",
"Maks Ovsjanikov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rakotosaona_Learning_Delaunay_Surface_Elements_for_Mesh_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rakotosaona_Learning_Delaunay_Surface_Elements_for_Mesh_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rakotosaona_Learning_Delaunay_Surface_CVPR_2021_supplemental.pdf | 2012.01203 | cvf | @InProceedings{Rakotosaona_2021_CVPR,
author = {Rakotosaona, Marie-Julie and Guerrero, Paul and Aigerman, Noam and Mitra, Niloy J. and Ovsjanikov, Maks},
title = {Learning Delaunay Surface Elements for Mesh Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it hard to create manifold meshes. We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.... |
Tong_FaceSec_A_Fine-Grained_Robustness_Evaluation_Framework_for_Face_Recognition_Systems_CVPR_2021_paper | FaceSec: A Fine-Grained Robustness Evaluation Framework for Face Recognition Systems | [
"Liang Tong",
"Zhengzhang Chen",
"Jingchao Ni",
"Wei Cheng",
"Dongjin Song",
"Haifeng Chen",
"Yevgeniy Vorobeychik"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tong_FaceSec_A_Fine-Grained_Robustness_Evaluation_Framework_for_Face_Recognition_Systems_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tong_FaceSec_A_Fine-Grained_Robustness_Evaluation_Framework_for_Face_Recognition_Systems_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tong_FaceSec_A_Fine-Grained_CVPR_2021_supplemental.pdf | 2104.04107 | cvf | @InProceedings{Tong_2021_CVPR,
author = {Tong, Liang and Chen, Zhengzhang and Ni, Jingchao and Cheng, Wei and Song, Dongjin and Chen, Haifeng and Vorobeychik, Yevgeniy},
title = {FaceSec: A Fine-Grained Robustness Evaluation Framework for Face Recognition Systems},
booktitle = {Proceedings of the IEE... | We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker's system knowledge (about training data and learning archite... |
Dai_Dynamic_Head_Unifying_Object_Detection_Heads_With_Attentions_CVPR_2021_paper | Dynamic Head: Unifying Object Detection Heads With Attentions | [
"Xiyang Dai",
"Yinpeng Chen",
"Bin Xiao",
"Dongdong Chen",
"Mengchen Liu",
"Lu Yuan",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Dynamic_Head_Unifying_Object_Detection_Heads_With_Attentions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Dynamic_Head_Unifying_Object_Detection_Heads_With_Attentions_CVPR_2021_paper.pdf | null | 2106.08322 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Xiyang and Chen, Yinpeng and Xiao, Bin and Chen, Dongdong and Liu, Mengchen and Yuan, Lu and Zhang, Lei},
title = {Dynamic Head: Unifying Object Detection Heads With Attentions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to u... |
Bai_Riggable_3D_Face_Reconstruction_via_In-Network_Optimization_CVPR_2021_paper | Riggable 3D Face Reconstruction via In-Network Optimization | [
"Ziqian Bai",
"Zhaopeng Cui",
"Xiaoming Liu",
"Ping Tan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Riggable_3D_Face_Reconstruction_via_In-Network_Optimization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Riggable_3D_Face_Reconstruction_via_In-Network_Optimization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_Riggable_3D_Face_CVPR_2021_supplemental.pdf | 2104.03493 | cvf | @InProceedings{Bai_2021_CVPR,
author = {Bai, Ziqian and Cui, Zhaopeng and Liu, Xiaoming and Tan, Ping},
title = {Riggable 3D Face Reconstruction via In-Network Optimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations. To achieve this goal, we design an end-to-end trainable network embedded with a differentiable in-network opti... |
Wang_One-Shot_Free-View_Neural_Talking-Head_Synthesis_for_Video_Conferencing_CVPR_2021_paper | One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing | [
"Ting-Chun Wang",
"Arun Mallya",
"Ming-Yu Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_One-Shot_Free-View_Neural_Talking-Head_Synthesis_for_Video_Conferencing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_One-Shot_Free-View_Neural_Talking-Head_Synthesis_for_Video_Conferencing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_One-Shot_Free-View_Neural_CVPR_2021_supplemental.pdf | 2011.15126 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Ting-Chun and Mallya, Arun and Liu, Ming-Yu},
title = {One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source image containing the target person's appearance and a driving video that dictates the motion in the output. Our motion is encoded based on a no... |
Chen_S2R-DepthNet_Learning_a_Generalizable_Depth-Specific_Structural_Representation_CVPR_2021_paper | S2R-DepthNet: Learning a Generalizable Depth-Specific Structural Representation | [
"Xiaotian Chen",
"Yuwang Wang",
"Xuejin Chen",
"Wenjun Zeng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_S2R-DepthNet_Learning_a_Generalizable_Depth-Specific_Structural_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_S2R-DepthNet_Learning_a_Generalizable_Depth-Specific_Structural_Representation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_S2R-DepthNet_Learning_a_CVPR_2021_supplemental.pdf | 2104.00877 | title_snapshot | @InProceedings{Chen_2021_CVPR,
author = {Chen, Xiaotian and Wang, Yuwang and Chen, Xuejin and Zeng, Wenjun},
title = {S2R-DepthNet: Learning a Generalizable Depth-Specific Structural Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature fo... |
Weng_Holistic_3D_Human_and_Scene_Mesh_Estimation_From_Single_View_CVPR_2021_paper | Holistic 3D Human and Scene Mesh Estimation From Single View Images | [
"Zhenzhen Weng",
"Serena Yeung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Weng_Holistic_3D_Human_and_Scene_Mesh_Estimation_From_Single_View_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Weng_Holistic_3D_Human_and_Scene_Mesh_Estimation_From_Single_View_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Weng_Holistic_3D_Human_CVPR_2021_supplemental.pdf | 2012.01591 | cvf | @InProceedings{Weng_2021_CVPR,
author = {Weng, Zhenzhen and Yeung, Serena},
title = {Holistic 3D Human and Scene Mesh Estimation From Single View Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | The 3D world limits the human body pose and the human body pose conveys information about the surrounding objects. Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior per... |
Angles_MIST_Multiple_Instance_Spatial_Transformer_CVPR_2021_paper | MIST: Multiple Instance Spatial Transformer | [
"Baptiste Angles",
"Yuhe Jin",
"Simon Kornblith",
"Andrea Tagliasacchi",
"Kwang Moo Yi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Angles_MIST_Multiple_Instance_Spatial_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Angles_MIST_Multiple_Instance_Spatial_Transformer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Angles_MIST_Multiple_Instance_CVPR_2021_supplemental.pdf | 1811.10725 | cvf | @InProceedings{Angles_2021_CVPR,
author = {Angles, Baptiste and Jin, Yuhe and Kornblith, Simon and Tagliasacchi, Andrea and Yi, Kwang Moo},
title = {MIST: Multiple Instance Spatial Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific ... |
He_FFB6D_A_Full_Flow_Bidirectional_Fusion_Network_for_6D_Pose_CVPR_2021_paper | FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation | [
"Yisheng He",
"Haibin Huang",
"Haoqiang Fan",
"Qifeng Chen",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_FFB6D_A_Full_Flow_Bidirectional_Fusion_Network_for_6D_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_FFB6D_A_Full_Flow_Bidirectional_Fusion_Network_for_6D_Pose_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_FFB6D_A_Full_CVPR_2021_supplemental.pdf | 2103.02242 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Yisheng and Huang, Haibin and Fan, Haoqiang and Chen, Qifeng and Sun, Jian},
title = {FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | In this work, we present FFB6D, a full flow bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in the RGB image and geometry information from the depth image are two complementary data sources, and it still remains unknown how to fully l... |
Ichikawa_Shape_From_Sky_Polarimetric_Normal_Recovery_Under_the_Sky_CVPR_2021_paper | Shape From Sky: Polarimetric Normal Recovery Under the Sky | [
"Tomoki Ichikawa",
"Matthew Purri",
"Ryo Kawahara",
"Shohei Nobuhara",
"Kristin Dana",
"Ko Nishino"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ichikawa_Shape_From_Sky_Polarimetric_Normal_Recovery_Under_the_Sky_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ichikawa_Shape_From_Sky_Polarimetric_Normal_Recovery_Under_the_Sky_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ichikawa_Shape_From_Sky_CVPR_2021_supplemental.zip | null | null | @InProceedings{Ichikawa_2021_CVPR,
author = {Ichikawa, Tomoki and Purri, Matthew and Kawahara, Ryo and Nobuhara, Shohei and Dana, Kristin and Nishino, Ko},
title = {Shape From Sky: Polarimetric Normal Recovery Under the Sky},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | The sky exhibits a unique spatial polarization pattern by scattering the unpolarized sun light. Just like insects use this unique angular pattern to navigate, we use it to map pixels to directions on the sky. That is, we show that the unique polarization pattern encoded in the polarimetric appearance of an object captu... |
Fan_Adversarially_Adaptive_Normalization_for_Single_Domain_Generalization_CVPR_2021_paper | Adversarially Adaptive Normalization for Single Domain Generalization | [
"Xinjie Fan",
"Qifei Wang",
"Junjie Ke",
"Feng Yang",
"Boqing Gong",
"Mingyuan Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Adversarially_Adaptive_Normalization_for_Single_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Adversarially_Adaptive_Normalization_for_Single_Domain_Generalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fan_Adversarially_Adaptive_Normalization_CVPR_2021_supplemental.pdf | 2106.01899 | cvf | @InProceedings{Fan_2021_CVPR,
author = {Fan, Xinjie and Wang, Qifei and Ke, Junjie and Yang, Feng and Gong, Boqing and Zhou, Mingyuan},
title = {Adversarially Adaptive Normalization for Single Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capability. The impact on domain generalization from the statistics of nor... |
Han_Rethinking_Channel_Dimensions_for_Efficient_Model_Design_CVPR_2021_paper | Rethinking Channel Dimensions for Efficient Model Design | [
"Dongyoon Han",
"Sangdoo Yun",
"Byeongho Heo",
"YoungJoon Yoo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Han_Rethinking_Channel_Dimensions_for_Efficient_Model_Design_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_Rethinking_Channel_Dimensions_for_Efficient_Model_Design_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Han_Rethinking_Channel_Dimensions_CVPR_2021_supplemental.pdf | 2007.00992 | cvf | @InProceedings{Han_2021_CVPR,
author = {Han, Dongyoon and Yun, Sangdoo and Heo, Byeongho and Yoo, YoungJoon},
title = {Rethinking Channel Dimensions for Efficient Model Design},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Designing an efficient model within the limited computational cost is challenging. We argue the accuracy of a lightweight model has been further limited by the design convention: a stage-wise configuration of the channel dimensions, which looks like a piecewise linear function of the network stage. In this paper, we st... |
Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper | A Self-Boosting Framework for Automated Radiographic Report Generation | [
"Zhanyu Wang",
"Luping Zhou",
"Lei Wang",
"Xiu Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Zhanyu and Zhou, Luping and Wang, Lei and Li, Xiu},
title = {A Self-Boosting Framework for Automated Radiographic Report Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Automated radiographic report generation is a challenging task since it requires to generate paragraphs describing fine-grained visual differences of cases, especially for those between the diseased and the healthy. Existing image captioning methods commonly target at generic images, and lack mechanism to meet this req... |
Teed_RAFT-3D_Scene_Flow_Using_Rigid-Motion_Embeddings_CVPR_2021_paper | RAFT-3D: Scene Flow Using Rigid-Motion Embeddings | [
"Zachary Teed",
"Jia Deng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Teed_RAFT-3D_Scene_Flow_Using_Rigid-Motion_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Teed_RAFT-3D_Scene_Flow_Using_Rigid-Motion_Embeddings_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Teed_RAFT-3D_Scene_Flow_CVPR_2021_supplemental.pdf | 2012.00726 | title_snapshot | @InProceedings{Teed_2021_CVPR,
author = {Teed, Zachary and Deng, Jia},
title = {RAFT-3D: Scene Flow Using Rigid-Motion Embeddings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {... | We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT-3D is based on the RAFT model developed for optical flow but iteratively updates a dense field of pixelwise SE3 motion instead of 2D motion... |
Liu_Orthogonal_Over-Parameterized_Training_CVPR_2021_paper | Orthogonal Over-Parameterized Training | [
"Weiyang Liu",
"Rongmei Lin",
"Zhen Liu",
"James M. Rehg",
"Liam Paull",
"Li Xiong",
"Le Song",
"Adrian Weller"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Orthogonal_Over-Parameterized_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Orthogonal_Over-Parameterized_Training_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Orthogonal_Over-Parameterized_Training_CVPR_2021_supplemental.pdf | 2004.04690 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Weiyang and Lin, Rongmei and Liu, Zhen and Rehg, James M. and Paull, Liam and Xiong, Li and Song, Le and Weller, Adrian},
title = {Orthogonal Over-Parameterized Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hypersph... |
Durasov_Masksembles_for_Uncertainty_Estimation_CVPR_2021_paper | Masksembles for Uncertainty Estimation | [
"Nikita Durasov",
"Timur Bagautdinov",
"Pierre Baque",
"Pascal Fua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Durasov_Masksembles_for_Uncertainty_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Durasov_Masksembles_for_Uncertainty_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Durasov_Masksembles_for_Uncertainty_CVPR_2021_supplemental.pdf | 2012.08334 | cvf | @InProceedings{Durasov_2021_CVPR,
author = {Durasov, Nikita and Bagautdinov, Timur and Baque, Pierre and Fua, Pascal},
title = {Masksembles for Uncertainty Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expensive to train and evaluate. MC-Dropout is another popular alter... |
Gao_Network_Pruning_via_Performance_Maximization_CVPR_2021_paper | Network Pruning via Performance Maximization | [
"Shangqian Gao",
"Feihu Huang",
"Weidong Cai",
"Heng Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Network_Pruning_via_Performance_Maximization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Network_Pruning_via_Performance_Maximization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Network_Pruning_via_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Gao_2021_CVPR,
author = {Gao, Shangqian and Huang, Feihu and Cai, Weidong and Huang, Heng},
title = {Network Pruning via Performance Maximization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Channel pruning is a class of powerful methods for model compression. When pruning a neural network, it's ideal to obtain a sub-network with higher accuracy. However, a sub-network does not necessarily have high accuracy with low classification loss (loss-metric mismatch). In the paper, we first consider the loss-metri... |
Ye_Closing_the_Loop_Joint_Rain_Generation_and_Removal_via_Disentangled_CVPR_2021_paper | Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation | [
"Yuntong Ye",
"Yi Chang",
"Hanyu Zhou",
"Luxin Yan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Closing_the_Loop_Joint_Rain_Generation_and_Removal_via_Disentangled_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Closing_the_Loop_Joint_Rain_Generation_and_Removal_via_Disentangled_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Closing_the_Loop_CVPR_2021_supplemental.pdf | 2103.13660 | cvf | @InProceedings{Ye_2021_CVPR,
author = {Ye, Yuntong and Chang, Yi and Zhou, Hanyu and Yan, Luxin},
title = {Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts. However, these methods suffer from significant performance drop when facing the real rain, because of the huge gap between th... |
Wang_ACTION-Net_Multipath_Excitation_for_Action_Recognition_CVPR_2021_paper | ACTION-Net: Multipath Excitation for Action Recognition | [
"Zhengwei Wang",
"Qi She",
"Aljosa Smolic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ACTION-Net_Multipath_Excitation_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_ACTION-Net_Multipath_Excitation_for_Action_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_ACTION-Net_Multipath_Excitation_CVPR_2021_supplemental.pdf | 2103.07372 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Zhengwei and She, Qi and Smolic, Aljosa},
title = {ACTION-Net: Multipath Excitation for Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {... | Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relationships; 3D CNNs can achieve good performance but are computationally intensive. In this work, we tack... |
Wiles_Co-Attention_for_Conditioned_Image_Matching_CVPR_2021_paper | Co-Attention for Conditioned Image Matching | [
"Olivia Wiles",
"Sebastien Ehrhardt",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wiles_Co-Attention_for_Conditioned_Image_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wiles_Co-Attention_for_Conditioned_Image_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wiles_Co-Attention_for_Conditioned_CVPR_2021_supplemental.zip | 2007.08480 | cvf | @InProceedings{Wiles_2021_CVPR,
author = {Wiles, Olivia and Ehrhardt, Sebastien and Zisserman, Andrew},
title = {Co-Attention for Conditioned Image Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material. While other approaches find correspondences between pairs of images by treating the images independently, we instead condition on both images to implicitly take a... |
Duan_EventZoom_Learning_To_Denoise_and_Super_Resolve_Neuromorphic_Events_CVPR_2021_paper | EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events | [
"Peiqi Duan",
"Zihao W. Wang",
"Xinyu Zhou",
"Yi Ma",
"Boxin Shi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_EventZoom_Learning_To_Denoise_and_Super_Resolve_Neuromorphic_Events_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_EventZoom_Learning_To_Denoise_and_Super_Resolve_Neuromorphic_Events_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_EventZoom_Learning_To_CVPR_2021_supplemental.zip | null | null | @InProceedings{Duan_2021_CVPR,
author = {Duan, Peiqi and Wang, Zihao W. and Zhou, Xinyu and Ma, Yi and Shi, Boxin},
title = {EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We address the problem of jointly denoising and super resolving neuromorphic events, a novel visual signal that represents thresholded temporal gradients in a space-time window. The challenge for event signal processing is that they are asynchronously generated, and do not carry absolute intensity but only binary signs... |
Yun_Re-Labeling_ImageNet_From_Single_to_Multi-Labels_From_Global_to_Localized_CVPR_2021_paper | Re-Labeling ImageNet: From Single to Multi-Labels, From Global to Localized Labels | [
"Sangdoo Yun",
"Seong Joon Oh",
"Byeongho Heo",
"Dongyoon Han",
"Junsuk Choe",
"Sanghyuk Chun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yun_Re-Labeling_ImageNet_From_Single_to_Multi-Labels_From_Global_to_Localized_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yun_Re-Labeling_ImageNet_From_Single_to_Multi-Labels_From_Global_to_Localized_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yun_Re-Labeling_ImageNet_From_CVPR_2021_supplemental.pdf | 2101.05022 | cvf | @InProceedings{Yun_2021_CVPR,
author = {Yun, Sangdoo and Oh, Seong Joon and Heo, Byeongho and Han, Dongyoon and Choe, Junsuk and Chun, Sanghyuk},
title = {Re-Labeling ImageNet: From Single to Multi-Labels, From Global to Localized Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | ImageNet has been the most popular image classification benchmark, but it is also the one with a significant level of label noise. Recent studies have shown that many samples contain multiple classes, despite being assumed to be a single-label benchmark. They have thus proposed to turn ImageNet evaluation into a multi-... |
Zhou_CoCosNet_v2_Full-Resolution_Correspondence_Learning_for_Image_Translation_CVPR_2021_paper | CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation | [
"Xingran Zhou",
"Bo Zhang",
"Ting Zhang",
"Pan Zhang",
"Jianmin Bao",
"Dong Chen",
"Zhongfei Zhang",
"Fang Wen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_CoCosNet_v2_Full-Resolution_Correspondence_Learning_for_Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_CoCosNet_v2_Full-Resolution_Correspondence_Learning_for_Image_Translation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_CoCosNet_v2_Full-Resolution_CVPR_2021_supplemental.pdf | 2012.02047 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Xingran and Zhang, Bo and Zhang, Ting and Zhang, Pan and Bao, Jianmin and Chen, Dong and Zhang, Zhongfei and Wen, Fang},
title = {CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation},
booktitle = {Proceedings of the IEEE/CVF Confer... | We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the fine levels. At each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively levera... |
Wu_SceneGraphFusion_Incremental_3D_Scene_Graph_Prediction_From_RGB-D_Sequences_CVPR_2021_paper | SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences | [
"Shun-Cheng Wu",
"Johanna Wald",
"Keisuke Tateno",
"Nassir Navab",
"Federico Tombari"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_SceneGraphFusion_Incremental_3D_Scene_Graph_Prediction_From_RGB-D_Sequences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_SceneGraphFusion_Incremental_3D_Scene_Graph_Prediction_From_RGB-D_Sequences_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_SceneGraphFusion_Incremental_3D_CVPR_2021_supplemental.pdf | 2103.14898 | title_snapshot | @InProceedings{Wu_2021_CVPR,
author = {Wu, Shun-Cheng and Wald, Johanna and Tateno, Keisuke and Navab, Nassir and Tombari, Federico},
title = {SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to build up semantic scene graphs from a 3D environment incrementally given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene comp... |
Nan_Interventional_Video_Grounding_With_Dual_Contrastive_Learning_CVPR_2021_paper | Interventional Video Grounding With Dual Contrastive Learning | [
"Guoshun Nan",
"Rui Qiao",
"Yao Xiao",
"Jun Liu",
"Sicong Leng",
"Hao Zhang",
"Wei Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nan_Interventional_Video_Grounding_With_Dual_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nan_Interventional_Video_Grounding_With_Dual_Contrastive_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nan_Interventional_Video_Grounding_CVPR_2021_supplemental.pdf | 2106.11013 | cvf | @InProceedings{Nan_2021_CVPR,
author = {Nan, Guoshun and Qiao, Rui and Xiao, Yao and Liu, Jun and Leng, Sicong and Zhang, Hao and Lu, Wei},
title = {Interventional Video Grounding With Dual Contrastive Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y|X). Consequently, these models may suffer from spurious correlations betw... |
Xu_A_Fourier-Based_Framework_for_Domain_Generalization_CVPR_2021_paper | A Fourier-Based Framework for Domain Generalization | [
"Qinwei Xu",
"Ruipeng Zhang",
"Ya Zhang",
"Yanfeng Wang",
"Qi Tian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_A_Fourier-Based_Framework_for_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_A_Fourier-Based_Framework_for_Domain_Generalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_A_Fourier-Based_Framework_CVPR_2021_supplemental.pdf | 2105.11120 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Qinwei and Zhang, Ruipeng and Zhang, Ya and Wang, Yanfeng and Tian, Qi},
title = {A Fourier-Based Framework for Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at tackling this problem by learning transferable knowledge from multiple source domains in order to generalize to unseen target domains. This paper... |
Yang_Probabilistic_Modeling_of_Semantic_Ambiguity_for_Scene_Graph_Generation_CVPR_2021_paper | Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation | [
"Gengcong Yang",
"Jingyi Zhang",
"Yong Zhang",
"Baoyuan Wu",
"Yujiu Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Probabilistic_Modeling_of_Semantic_Ambiguity_for_Scene_Graph_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Probabilistic_Modeling_of_Semantic_Ambiguity_for_Scene_Graph_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Probabilistic_Modeling_of_CVPR_2021_supplemental.pdf | 2103.05271 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Gengcong and Zhang, Jingyi and Zhang, Yong and Wu, Baoyuan and Yang, Yujiu},
title = {Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | To generate "accurate" scene graphs, almost all exist-ing methods predict pairwise relationships in a determin-istic manner. However, we argue that visual relationshipsare often semantically ambiguous. Specifically, inspired bylinguistic knowledge, we classify the ambiguity into threetypes: Synonymy Ambiguity, Hyponymy... |
Son_SRWarp_Generalized_Image_Super-Resolution_under_Arbitrary_Transformation_CVPR_2021_paper | SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation | [
"Sanghyun Son",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Son_SRWarp_Generalized_Image_Super-Resolution_under_Arbitrary_Transformation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Son_SRWarp_Generalized_Image_Super-Resolution_under_Arbitrary_Transformation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Son_SRWarp_Generalized_Image_CVPR_2021_supplemental.pdf | 2104.10325 | cvf | @InProceedings{Son_2021_CVPR,
author = {Son, Sanghyun and Lee, Kyoung Mu},
title = {SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., x2 or x4. Thus, they are difficult to be applied when arbitrary target resolutions are req... |
Ma_IQDet_Instance-Wise_Quality_Distribution_Sampling_for_Object_Detection_CVPR_2021_paper | IQDet: Instance-Wise Quality Distribution Sampling for Object Detection | [
"Yuchen Ma",
"Songtao Liu",
"Zeming Li",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_IQDet_Instance-Wise_Quality_Distribution_Sampling_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_IQDet_Instance-Wise_Quality_Distribution_Sampling_for_Object_Detection_CVPR_2021_paper.pdf | null | 2104.06936 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Yuchen and Liu, Songtao and Li, Zeming and Sun, Jian},
title = {IQDet: Instance-Wise Quality Distribution Sampling for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | We propose a dense object detector with an instance-wise sampling strategy, named IQDet. Instead of using human prior sampling strategies, we first extract the regional feature of each ground-truth to estimate the instance-wise quality distribution. According to a mixture model in spatial dimensions, the distribution i... |
Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper | Scan2Cap: Context-Aware Dense Captioning in RGB-D Scans | [
"Zhenyu Chen",
"Ali Gholami",
"Matthias Niessner",
"Angel X. Chang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Scan2Cap_Context-Aware_Dense_CVPR_2021_supplemental.pdf | 2012.02206 | title_snapshot | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zhenyu and Gholami, Ali and Niessner, Matthias and Chang, Angel X.},
title = {Scan2Cap: Context-Aware Dense Captioning in RGB-D Scans},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We introduce the new task of dense captioning in RGB-D scans. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detecting and describing problem at the same time, we propose Scan2Cap, an end-to-end ... |
Suo_NeuralHumanFVV_Real-Time_Neural_Volumetric_Human_Performance_Rendering_Using_RGB_Cameras_CVPR_2021_paper | NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering Using RGB Cameras | [
"Xin Suo",
"Yuheng Jiang",
"Pei Lin",
"Yingliang Zhang",
"Minye Wu",
"Kaiwen Guo",
"Lan Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Suo_NeuralHumanFVV_Real-Time_Neural_Volumetric_Human_Performance_Rendering_Using_RGB_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Suo_NeuralHumanFVV_Real-Time_Neural_Volumetric_Human_Performance_Rendering_Using_RGB_Cameras_CVPR_2021_paper.pdf | null | 2103.07700 | cvf | @InProceedings{Suo_2021_CVPR,
author = {Suo, Xin and Jiang, Yuheng and Lin, Pei and Zhang, Yingliang and Wu, Minye and Guo, Kaiwen and Xu, Lan},
title = {NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering Using RGB Cameras},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | 4D reconstruction and rendering of human activities is critical for immersive VR/AR experience. Recent advances still fail to recover fine geometry and texture results with the level of detail present in the input images from sparse multi-view RGB cameras. In this paper, we propose NeuralHumanFVV, a real-time neural hu... |
Liu_Anti-Aliasing_Semantic_Reconstruction_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper | Anti-Aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation | [
"Binghao Liu",
"Yao Ding",
"Jianbin Jiao",
"Xiangyang Ji",
"Qixiang Ye"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Anti-Aliasing_Semantic_Reconstruction_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Anti-Aliasing_Semantic_Reconstruction_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.pdf | null | 2106.00184 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Binghao and Ding, Yao and Jiao, Jianbin and Ji, Xiangyang and Ye, Qixiang},
title = {Anti-Aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples. However, this feature sharing mechanism inevitably causes semantic aliasing between novel classes when they have similar ... |
Hong_Composing_Photos_Like_a_Photographer_CVPR_2021_paper | Composing Photos Like a Photographer | [
"Chaoyi Hong",
"Shuaiyuan Du",
"Ke Xian",
"Hao Lu",
"Zhiguo Cao",
"Weicai Zhong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Composing_Photos_Like_a_Photographer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Composing_Photos_Like_a_Photographer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Composing_Photos_Like_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hong_2021_CVPR,
author = {Hong, Chaoyi and Du, Shuaiyuan and Xian, Ke and Lu, Hao and Cao, Zhiguo and Zhong, Weicai},
title = {Composing Photos Like a Photographer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | We show that explicit modeling of composition rules benefits image cropping. Image cropping is considered a promising way to automate aesthetic composition in professional photography. Existing efforts, however, only model such professional knowledge implicitly, e.g., by ranking from comparative candidates. Inspired by... |
Cui_Asymmetric_Gained_Deep_Image_Compression_With_Continuous_Rate_Adaptation_CVPR_2021_paper | Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation | [
"Ze Cui",
"Jing Wang",
"Shangyin Gao",
"Tiansheng Guo",
"Yihui Feng",
"Bo Bai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Asymmetric_Gained_Deep_Image_Compression_With_Continuous_Rate_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Asymmetric_Gained_Deep_Image_Compression_With_Continuous_Rate_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cui_Asymmetric_Gained_Deep_CVPR_2021_supplemental.pdf | 2003.02012 | title_snapshot | @InProceedings{Cui_2021_CVPR,
author = {Cui, Ze and Wang, Jing and Gao, Shangyin and Guo, Tiansheng and Feng, Yihui and Bai, Bo},
title = {Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. Recently, learned image compression methods had exceeded their classical counterparts in terms of rate-distortion performance. However, continuous rate adaptation remains an open questi... |
Feng_Optimal_Gradient_Checkpoint_Search_for_Arbitrary_Computation_Graphs_CVPR_2021_paper | Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs | [
"Jianwei Feng",
"Dong Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Optimal_Gradient_Checkpoint_Search_for_Arbitrary_Computation_Graphs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Optimal_Gradient_Checkpoint_Search_for_Arbitrary_Computation_Graphs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feng_Optimal_Gradient_Checkpoint_CVPR_2021_supplemental.pdf | 1808.00079 | cvf | @InProceedings{Feng_2021_CVPR,
author = {Feng, Jianwei and Huang, Dong},
title = {Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is physically finite, which limits the image resolutions and batch sizes that could be used in training for better DNN performance. Unlike solutions that require physically upgrade GPUs, the G... |
Cheng_NBNet_Noise_Basis_Learning_for_Image_Denoising_With_Subspace_Projection_CVPR_2021_paper | NBNet: Noise Basis Learning for Image Denoising With Subspace Projection | [
"Shen Cheng",
"Yuzhi Wang",
"Haibin Huang",
"Donghao Liu",
"Haoqiang Fan",
"Shuaicheng Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_NBNet_Noise_Basis_Learning_for_Image_Denoising_With_Subspace_Projection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_NBNet_Noise_Basis_Learning_for_Image_Denoising_With_Subspace_Projection_CVPR_2021_paper.pdf | null | 2012.15028 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Shen and Wang, Yuzhi and Huang, Haibin and Liu, Donghao and Fan, Haoqiang and Liu, Shuaicheng},
title = {NBNet: Noise Basis Learning for Image Denoising With Subspace Projection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reduction by image-adaptive projection. Specifically, we propose to train a network that can separate signal and noise by learning a set of reconst... |
Srinivasan_NeRV_Neural_Reflectance_and_Visibility_Fields_for_Relighting_and_View_CVPR_2021_paper | NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis | [
"Pratul P. Srinivasan",
"Boyang Deng",
"Xiuming Zhang",
"Matthew Tancik",
"Ben Mildenhall",
"Jonathan T. Barron"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Srinivasan_NeRV_Neural_Reflectance_and_Visibility_Fields_for_Relighting_and_View_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Srinivasan_NeRV_Neural_Reflectance_and_Visibility_Fields_for_Relighting_and_View_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Srinivasan_NeRV_Neural_Reflectance_CVPR_2021_supplemental.pdf | 2012.03927 | cvf | @InProceedings{Srinivasan_2021_CVPR,
author = {Srinivasan, Pratul P. and Deng, Boyang and Zhang, Xiuming and Tancik, Matthew and Mildenhall, Ben and Barron, Jonathan T.},
title = {NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis},
booktitle = {Proceedings of the IEEE/C... | We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as ... |
Kervadec_How_Transferable_Are_Reasoning_Patterns_in_VQA_CVPR_2021_paper | How Transferable Are Reasoning Patterns in VQA? | [
"Corentin Kervadec",
"Theo Jaunet",
"Grigory Antipov",
"Moez Baccouche",
"Romain Vuillemot",
"Christian Wolf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kervadec_How_Transferable_Are_Reasoning_Patterns_in_VQA_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kervadec_How_Transferable_Are_Reasoning_Patterns_in_VQA_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kervadec_How_Transferable_Are_CVPR_2021_supplemental.zip | 2104.03656 | cvf | @InProceedings{Kervadec_2021_CVPR,
author = {Kervadec, Corentin and Jaunet, Theo and Antipov, Grigory and Baccouche, Moez and Vuillemot, Romain and Wolf, Christian},
title = {How Transferable Are Reasoning Patterns in VQA?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing high-level reasoning. Classical methods address this by removing biases from training data, or adding branches to models to detect and remove ... |
Yang_DyStaB_Unsupervised_Object_Segmentation_via_Dynamic-Static_Bootstrapping_CVPR_2021_paper | DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping | [
"Yanchao Yang",
"Brian Lai",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_DyStaB_Unsupervised_Object_Segmentation_via_Dynamic-Static_Bootstrapping_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_DyStaB_Unsupervised_Object_Segmentation_via_Dynamic-Static_Bootstrapping_CVPR_2021_paper.pdf | null | 2008.07012 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Yanchao and Lai, Brian and Soatto, Stefano},
title = {DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the motion field by minimizing the mutual information between segments. Then, it uses the segments to lea... |
Chen_Deep_Texture_Recognition_via_Exploiting_Cross-Layer_Statistical_Self-Similarity_CVPR_2021_paper | Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity | [
"Zhile Chen",
"Feng Li",
"Yuhui Quan",
"Yong Xu",
"Hui Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Deep_Texture_Recognition_via_Exploiting_Cross-Layer_Statistical_Self-Similarity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Deep_Texture_Recognition_via_Exploiting_Cross-Layer_Statistical_Self-Similarity_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Deep_Texture_Recognition_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zhile and Li, Feng and Quan, Yuhui and Xu, Yong and Ji, Hui},
title = {Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Ag... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.