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Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper | Bidirectional Projection Network for Cross Dimension Scene Understanding | [
"Wenbo Hu",
"Hengshuang Zhao",
"Li Jiang",
"Jiaya Jia",
"Tien-Tsin Wong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Bidirectional_Projection_Network_for_Cross_Dimension_Scene_Understanding_CVPR_2021_paper.pdf | null | 2103.14326 | cvf | @InProceedings{Hu_2021_CVPR,
author = {Hu, Wenbo and Zhao, Hengshuang and Jiang, Li and Jia, Jiaya and Wong, Tien-Tsin},
title = {Bidirectional Projection Network for Cross Dimension Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | 2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. ... |
Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper | Event-Based Synthetic Aperture Imaging With a Hybrid Network | [
"Xiang Zhang",
"Wei Liao",
"Lei Yu",
"Wen Yang",
"Gui-Song Xia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Event-Based_Synthetic_Aperture_Imaging_With_a_Hybrid_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Event-Based_Synthetic_Aperture_CVPR_2021_supplemental.pdf | 2103.02376 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xiang and Liao, Wei and Yu, Lei and Yang, Wen and Xia, Gui-Song},
title = {Event-Based Synthetic Aperture Imaging With a Hybrid Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on co... |
Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper | RSG: A Simple but Effective Module for Learning Imbalanced Datasets | [
"Jianfeng Wang",
"Thomas Lukasiewicz",
"Xiaolin Hu",
"Jianfei Cai",
"Zhenghua Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_RSG_A_Simple_but_Effective_Module_for_Learning_Imbalanced_Datasets_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_RSG_A_Simple_CVPR_2021_supplemental.pdf | 2106.09859 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jianfeng and Lukasiewicz, Thomas and Hu, Xiaolin and Cai, Jianfei and Xu, Zhenghua},
title = {RSG: A Simple but Effective Module for Learning Imbalanced Datasets},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it ... |
Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper | Learning Statistical Texture for Semantic Segmentation | [
"Lanyun Zhu",
"Deyi Ji",
"Shiping Zhu",
"Weihao Gan",
"Wei Wu",
"Junjie Yan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Learning_Statistical_Texture_for_Semantic_Segmentation_CVPR_2021_paper.pdf | null | 2103.04133 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Lanyun and Ji, Deyi and Zhu, Shiping and Gan, Weihao and Wu, Wei and Yan, Junjie},
title = {Learning Statistical Texture for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-level texture features are directly skip-connected into the deeper layers. Nevertheless, texture features are not only about local structure, b... |
Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper | Neural Feature Search for RGB-Infrared Person Re-Identification | [
"Yehansen Chen",
"Lin Wan",
"Zhihang Li",
"Qianyan Jing",
"Zongyuan Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Neural_Feature_Search_for_RGB-Infrared_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Neural_Feature_Search_CVPR_2021_supplemental.pdf | 2104.02366 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yehansen and Wan, Lin and Li, Zhihang and Jing, Qianyan and Sun, Zongyuan},
title = {Neural Feature Search for RGB-Infrared Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve performance gains through manually-designed feature selection modules, which often require significant ... |
Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper | FP-NAS: Fast Probabilistic Neural Architecture Search | [
"Zhicheng Yan",
"Xiaoliang Dai",
"Peizhao Zhang",
"Yuandong Tian",
"Bichen Wu",
"Matt Feiszli"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_FP-NAS_Fast_Probabilistic_Neural_Architecture_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_FP-NAS_Fast_Probabilistic_CVPR_2021_supplemental.pdf | 2011.10949 | title_snapshot | @InProceedings{Yan_2021_CVPR,
author = {Yan, Zhicheng and Dai, Xiaoliang and Zhang, Peizhao and Tian, Yuandong and Wu, Bichen and Feiszli, Matt},
title = {FP-NAS: Fast Probabilistic Neural Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In contrast, Probabilistic NAS, such as PARSEC, learns a distribution over high-performing architectures, and uses only as much memory as needed... |
Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper | Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps | [
"Gautam Pai",
"Jing Ren",
"Simone Melzi",
"Peter Wonka",
"Maks Ovsjanikov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pai_Fast_Sinkhorn_Filters_Using_Matrix_Scaling_for_Non-Rigid_Shape_Correspondence_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pai_Fast_Sinkhorn_Filters_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Pai_2021_CVPR,
author = {Pai, Gautam and Ren, Jing and Melzi, Simone and Wonka, Peter and Ovsjanikov, Maks},
title = {Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for th... |
Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper | Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction | [
"Shanyan Guan",
"Jingwei Xu",
"Yunbo Wang",
"Bingbing Ni",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guan_Bilevel_Online_Adaptation_for_Out-of-Domain_Human_Mesh_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guan_Bilevel_Online_Adaptation_CVPR_2021_supplemental.pdf | 2103.16449 | cvf | @InProceedings{Guan_2021_CVPR,
author = {Guan, Shanyan and Xu, Jingwei and Wang, Yunbo and Ni, Bingbing and Yang, Xiaokang},
title = {Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backg... |
Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper | The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth | [
"Jamie Watson",
"Oisin Mac Aodha",
"Victor Prisacariu",
"Gabriel Brostow",
"Michael Firman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Watson_The_Temporal_Opportunist_Self-Supervised_Multi-Frame_Monocular_Depth_CVPR_2021_paper.pdf | null | 2104.14540 | cvf | @InProceedings{Watson_2021_CVPR,
author = {Watson, Jamie and Mac Aodha, Oisin and Prisacariu, Victor and Brostow, Gabriel and Firman, Michael},
title = {The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of ... |
Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper | Distribution-Aware Adaptive Multi-Bit Quantization | [
"Sijie Zhao",
"Tao Yue",
"Xuemei Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Distribution-Aware_Adaptive_Multi-Bit_Quantization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Distribution-Aware_Adaptive_Multi-Bit_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Sijie and Yue, Tao and Hu, Xuemei},
title = {Distribution-Aware Adaptive Multi-Bit Quantization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs). A distribution-aware multi-bit quantization (DMBQ) method that incorporates the distribution prior into the optimization of quantization is proposed. Instead of solving the ... |
Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper | KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA | [
"Kenneth Marino",
"Xinlei Chen",
"Devi Parikh",
"Abhinav Gupta",
"Marcus Rohrbach"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Marino_KRISP_Integrating_Implicit_and_Symbolic_Knowledge_for_Open-Domain_Knowledge-Based_VQA_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Marino_KRISP_Integrating_Implicit_CVPR_2021_supplemental.pdf | 2012.11014 | cvf | @InProceedings{Marino_2021_CVPR,
author = {Marino, Kenneth and Chen, Xinlei and Parikh, Devi and Gupta, Abhinav and Rohrbach, Marcus},
title = {KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types... |
Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper | Amalgamating Knowledge From Heterogeneous Graph Neural Networks | [
"Yongcheng Jing",
"Yiding Yang",
"Xinchao Wang",
"Mingli Song",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Jing_2021_CVPR,
author = {Jing, Yongcheng and Yang, Yiding and Wang, Xinchao and Song, Mingli and Tao, Dacheng},
title = {Amalgamating Knowledge From Heterogeneous Graph Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs). We strive to train a multi-talented student GNN, without accessing human annotations, that "amalgamates" knowledge from a couple of teacher GNNs with heterogeneous architectures and handling distinct tasks. The studen... |
Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper | MetaSets: Meta-Learning on Point Sets for Generalizable Representations | [
"Chao Huang",
"Zhangjie Cao",
"Yunbo Wang",
"Jianmin Wang",
"Mingsheng Long"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MetaSets_Meta-Learning_on_Point_Sets_for_Generalizable_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MetaSets_Meta-Learning_on_CVPR_2021_supplemental.zip | 2204.07311 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Chao and Cao, Zhangjie and Wang, Yunbo and Wang, Jianmin and Long, Mingsheng},
title = {MetaSets: Meta-Learning on Point Sets for Generalizable Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critical to learn generalizable representations that can transfer well across different point sets. In this paper, we study a new problem of 3D Do... |
Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper | StEP: Style-Based Encoder Pre-Training for Multi-Modal Image Synthesis | [
"Moustafa Meshry",
"Yixuan Ren",
"Larry S. Davis",
"Abhinav Shrivastava"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Meshry_StEP_Style-Based_Encoder_Pre-Training_for_Multi-Modal_Image_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meshry_StEP_Style-Based_Encoder_CVPR_2021_supplemental.pdf | 2104.07098 | cvf | @InProceedings{Meshry_2021_CVPR,
author = {Meshry, Moustafa and Ren, Yixuan and Davis, Larry S. and Shrivastava, Abhinav},
title = {StEP: Style-Based Encoder Pre-Training for Multi-Modal Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex training objectives to learn a latent embedding, jointly with the generator, that models the variability of the output domain. In contrast, we... |
Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper | Goal-Oriented Gaze Estimation for Zero-Shot Learning | [
"Yang Liu",
"Lei Zhou",
"Xiao Bai",
"Yifei Huang",
"Lin Gu",
"Jun Zhou",
"Tatsuya Harada"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Goal-Oriented_Gaze_Estimation_for_Zero-Shot_Learning_CVPR_2021_paper.pdf | null | 2103.03433 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yang and Zhou, Lei and Bai, Xiao and Huang, Yifei and Gu, Lin and Zhou, Jun and Harada, Tatsuya},
title = {Goal-Oriented Gaze Estimation for Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes shared between different classes, which are highly local, strong prior for localization of object attribute is beneficial for visual-semantic e... |
Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper | LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering | [
"Fu-En Wang",
"Yu-Hsuan Yeh",
"Min Sun",
"Wei-Chen Chiu",
"Yi-Hsuan Tsai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_LED2-Net_Monocular_360deg_Layout_Estimation_via_Differentiable_Depth_Rendering_CVPR_2021_paper.pdf | null | 2104.00568 | title_judge | @InProceedings{Wang_2021_CVPR,
author = {Wang, Fu-En and Yeh, Yu-Hsuan and Sun, Min and Chiu, Wei-Chen and Tsai, Yi-Hsuan},
title = {LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth o... |
Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper | Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos | [
"Mingxing Zhang",
"Yang Yang",
"Xinghan Chen",
"Yanli Ji",
"Xing Xu",
"Jingjing Li",
"Heng Tao Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Multi-Stage_Aggregated_Transformer_Network_for_Temporal_Language_Localization_in_Videos_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Mingxing and Yang, Yang and Chen, Xinghan and Ji, Yanli and Xu, Xing and Li, Jingjing and Shen, Heng Tao},
title = {Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos},
booktitle = {Proceedings of the IEEE/CVF Confe... | We address the problem of localizing a specific moment from an untrimmed video by a language sentence query. Generally, previous methods mainly exist two problems that are not fully solved: 1) How to effectively model the fine-grained visual-language alignment between video and language query? 2) How to accurately loca... |
Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper | DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation | [
"Xinyi Wu",
"Zhenyao Wu",
"Hao Guo",
"Lili Ju",
"Song Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_DANNet_A_One-Stage_Domain_Adaptation_Network_for_Unsupervised_Nighttime_Semantic_CVPR_2021_paper.pdf | null | 2104.10834 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Xinyi and Wu, Zhenyao and Guo, Hao and Ju, Lili and Wang, Song},
title = {DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmenta... |
Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper | Dynamic Transfer for Multi-Source Domain Adaptation | [
"Yunsheng Li",
"Lu Yuan",
"Yinpeng Chen",
"Pei Wang",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Transfer_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | null | 2103.10583 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Yunsheng and Yuan, Lu and Chen, Yinpeng and Wang, Pei and Vasconcelos, Nuno},
title = {Dynamic Transfer for Multi-Source Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain. In this paper, we pres... |
Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper | Semi-Supervised Video Deraining With Dynamical Rain Generator | [
"Zongsheng Yue",
"Jianwen Xie",
"Qian Zhao",
"Deyu Meng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Semi-Supervised_Video_Deraining_CVPR_2021_supplemental.pdf | 2103.07939 | cvf | @InProceedings{Yue_2021_CVPR,
author = {Yue, Zongsheng and Xie, Jianwen and Zhao, Qian and Meng, Deyu},
title = {Semi-Supervised Video Deraining With Dynamical Rain Generator},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos. In fact, the rain layers exhibit strong physical properties (e.g., direction, scale... |
Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper | See Through Gradients: Image Batch Recovery via GradInversion | [
"Hongxu Yin",
"Arun Mallya",
"Arash Vahdat",
"Jose M. Alvarez",
"Jan Kautz",
"Pavlo Molchanov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_See_Through_Gradients_CVPR_2021_supplemental.pdf | 2104.07586 | cvf | @InProceedings{Yin_2021_CVPR,
author = {Yin, Hongxu and Mallya, Arun and Vahdat, Arash and Alvarez, Jose M. and Kautz, Jan and Molchanov, Pavlo},
title = {See Through Gradients: Image Batch Recovery via GradInversion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possib... |
Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper | Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition | [
"Delian Ruan",
"Yan Yan",
"Shenqi Lai",
"Zhenhua Chai",
"Chunhua Shen",
"Hanzi Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ruan_Feature_Decomposition_and_Reconstruction_Learning_for_Effective_Facial_Expression_Recognition_CVPR_2021_paper.pdf | null | 2104.05160 | cvf | @InProceedings{Ruan_2021_CVPR,
author = {Ruan, Delian and Yan, Yan and Lai, Shenqi and Chai, Zhenhua and Shen, Chunhua and Wang, Hanzi},
title = {Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-spe... |
Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper | Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences | [
"Norman Muller",
"Yu-Shiang Wong",
"Niloy J. Mitra",
"Angela Dai",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Muller_Seeing_Behind_Objects_CVPR_2021_supplemental.zip | 2012.08197 | title_snapshot | @InProceedings{Muller_2021_CVPR,
author = {Muller, Norman and Wong, Yu-Shiang and Mitra, Niloy J. and Dai, Angela and Niessner, Matthias},
title = {Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track t... |
Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper | Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks | [
"Xiaoxiao Long",
"Lingjie Liu",
"Wei Li",
"Christian Theobalt",
"Wenping Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Long_Multi-view_Depth_Estimation_using_Epipolar_Spatio-Temporal_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Long_Multi-view_Depth_Estimation_CVPR_2021_supplemental.pdf | 2011.13118 | cvf | @InProceedings{Long_2021_CVPR,
author = {Long, Xiaoxiao and Liu, Lingjie and Li, Wei and Theobalt, Christian and Wang, Wenping},
title = {Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation. Although previous learning-based methods have demonstrated compelling results, most works estimate depth maps of individual video fram... |
Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper | AutoFlow: Learning a Better Training Set for Optical Flow | [
"Deqing Sun",
"Daniel Vlasic",
"Charles Herrmann",
"Varun Jampani",
"Michael Krainin",
"Huiwen Chang",
"Ramin Zabih",
"William T. Freeman",
"Ce Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_AutoFlow_Learning_a_Better_Training_Set_for_Optical_Flow_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_AutoFlow_Learning_a_CVPR_2021_supplemental.zip | 2104.14544 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Deqing and Vlasic, Daniel and Herrmann, Charles and Jampani, Varun and Krainin, Michael and Chang, Huiwen and Zabih, Ramin and Freeman, William T. and Liu, Ce},
title = {AutoFlow: Learning a Better Training Set for Optical Flow},
booktitle = {Proceedings o... | Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model ... |
Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper | LPSNet: A Lightweight Solution for Fast Panoptic Segmentation | [
"Weixiang Hong",
"Qingpei Guo",
"Wei Zhang",
"Jingdong Chen",
"Wei Chu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_LPSNet_A_Lightweight_Solution_for_Fast_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Hong_2021_CVPR,
author = {Hong, Weixiang and Guo, Qingpei and Zhang, Wei and Chen, Jingdong and Chu, Wei},
title = {LPSNet: A Lightweight Solution for Fast Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize two-stage detection network to attain instance segmentation results, and fully convolutional network to produce semantic segm... |
Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper | You See What I Want You To See: Exploring Targeted Black-Box Transferability Attack for Hash-Based Image Retrieval Systems | [
"Yanru Xiao",
"Cong Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_You_See_What_I_Want_You_To_See_Exploring_Targeted_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_You_See_What_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Xiao_2021_CVPR,
author = {Xiao, Yanru and Wang, Cong},
title = {You See What I Want You To See: Exploring Targeted Black-Box Transferability Attack for Hash-Based Image Retrieval Systems},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic backend from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online image... |
Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper | The Blessings of Unlabeled Background in Untrimmed Videos | [
"Yuan Liu",
"Jingyuan Chen",
"Zhenfang Chen",
"Bing Deng",
"Jianqiang Huang",
"Hanwang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_The_Blessings_of_Unlabeled_Background_in_Untrimmed_Videos_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_The_Blessings_of_CVPR_2021_supplemental.pdf | 2103.13183 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yuan and Chen, Jingyuan and Chen, Zhenfang and Deng, Bing and Huang, Jianqiang and Zhang, Hanwang},
title = {The Blessings of Unlabeled Background in Untrimmed Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as... |
Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper | Autoregressive Stylized Motion Synthesis With Generative Flow | [
"Yu-Hui Wen",
"Zhipeng Yang",
"Hongbo Fu",
"Lin Gao",
"Yanan Sun",
"Yong-Jin Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Autoregressive_Stylized_Motion_CVPR_2021_supplemental.zip | null | null | @InProceedings{Wen_2021_CVPR,
author = {Wen, Yu-Hui and Yang, Zhipeng and Fu, Hongbo and Gao, Lin and Sun, Yanan and Liu, Yong-Jin},
title = {Autoregressive Stylized Motion Synthesis With Generative Flow},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics. Most existing deep learning methods for this problem are supervised and trained by registered motion pairs. In addition, these methods are often limited to yielding a... |
Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper | Improving Multiple Object Tracking With Single Object Tracking | [
"Linyu Zheng",
"Ming Tang",
"Yingying Chen",
"Guibo Zhu",
"Jinqiao Wang",
"Hanqing Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Improving_Multiple_Object_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Linyu and Tang, Ming and Chen, Yingying and Zhu, Guibo and Wang, Jinqiao and Lu, Hanqing},
title = {Improving Multiple Object Tracking With Single Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple S... |
Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper | Memory Oriented Transfer Learning for Semi-Supervised Image Deraining | [
"Huaibo Huang",
"Aijing Yu",
"Ran He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Memory_Oriented_Transfer_Learning_for_Semi-Supervised_Image_Deraining_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Memory_Oriented_Transfer_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Huang_2021_CVPR,
author = {Huang, Huaibo and Yu, Aijing and He, Ran},
title = {Memory Oriented Transfer Learning for Semi-Supervised Image Deraining},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Deep learning based methods have shown dramatic improvements in image rain removal by using large-scale paired data of synthetic datasets. However, due to the various appearances of real rain streaks that may be different from those in the synthetic training data, it is challenging to directly extend existing methods t... |
Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper | Instance Localization for Self-Supervised Detection Pretraining | [
"Ceyuan Yang",
"Zhirong Wu",
"Bolei Zhou",
"Stephen Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Instance_Localization_for_Self-Supervised_Detection_Pretraining_CVPR_2021_paper.pdf | null | 2102.08318 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Ceyuan and Wu, Zhirong and Zhou, Bolei and Lin, Stephen},
title = {Instance Localization for Self-Supervised Detection Pretraining},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. The objective of this paper is to advance self-supervised pretrained models specifically for object detection. Based on the inherent difference between c... |
Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper | Adaptive Methods for Real-World Domain Generalization | [
"Abhimanyu Dubey",
"Vignesh Ramanathan",
"Alex Pentland",
"Dhruv Mahajan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dubey_Adaptive_Methods_for_CVPR_2021_supplemental.pdf | 2103.15796 | cvf | @InProceedings{Dubey_2021_CVPR,
author = {Dubey, Abhimanyu and Ramanathan, Vignesh and Pentland, Alex and Mahajan, Dhruv},
title = {Adaptive Methods for Real-World Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the unseen test samples t... |
Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper | Deep Animation Video Interpolation in the Wild | [
"Li Siyao",
"Shiyu Zhao",
"Weijiang Yu",
"Wenxiu Sun",
"Dimitris Metaxas",
"Chen Change Loy",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Siyao_Deep_Animation_Video_Interpolation_in_the_Wild_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Siyao_Deep_Animation_Video_CVPR_2021_supplemental.pdf | 2104.02495 | cvf | @InProceedings{Siyao_2021_CVPR,
author = {Siyao, Li and Zhao, Shiyu and Yu, Weijiang and Sun, Wenxiu and Metaxas, Dimitris and Loy, Chen Change and Liu, Ziwei},
title = {Deep Animation Video Interpolation in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to develop computational models that can automatically interpolate the in-between animation frames. However, existing video interpolation methods fa... |
Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper | Isometric Multi-Shape Matching | [
"Maolin Gao",
"Zorah Lahner",
"Johan Thunberg",
"Daniel Cremers",
"Florian Bernard"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Isometric_Multi-Shape_Matching_CVPR_2021_supplemental.pdf | 2012.02689 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Maolin and Lahner, Zorah and Thunberg, Johan and Cremers, Daniel and Bernard, Florian},
title = {Isometric Multi-Shape Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instanc... |
Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper | Spatially Consistent Representation Learning | [
"Byungseok Roh",
"Wuhyun Shin",
"Ildoo Kim",
"Sungwoong Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Roh_Spatially_Consistent_Representation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Roh_Spatially_Consistent_Representation_CVPR_2021_supplemental.pdf | 2103.06122 | cvf | @InProceedings{Roh_2021_CVPR,
author = {Roh, Byungseok and Shin, Wuhyun and Kim, Ildoo and Kim, Sungwoong},
title = {Spatially Consistent Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representa... |
Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper | Semantic Scene Completion via Integrating Instances and Scene In-the-Loop | [
"Yingjie Cai",
"Xuesong Chen",
"Chao Zhang",
"Kwan-Yee Lin",
"Xiaogang Wang",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Semantic_Scene_Completion_CVPR_2021_supplemental.pdf | 2104.03640 | cvf | @InProceedings{Cai_2021_CVPR,
author = {Cai, Yingjie and Chen, Xuesong and Zhang, Chao and Lin, Kwan-Yee and Wang, Xiaogang and Li, Hongsheng},
title = {Semantic Scene Completion via Integrating Instances and Scene In-the-Loop},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. It is a crucial but challenging problem for indoor scene understanding. In this work, we present a novel framework named Scene-Instance-Scene Network (SISNet), which takes advan... |
Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper | Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation | [
"Ling Hu",
"Qinsong Li",
"Shengjun Liu",
"Xinru Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Efficient_Deformable_Shape_Correspondence_via_Multiscale_Spectral_Manifold_Wavelets_Preservation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Efficient_Deformable_Shape_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hu_2021_CVPR,
author = {Hu, Ling and Li, Qinsong and Liu, Shengjun and Liu, Xinru},
title = {Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct n... |
Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper | TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations | [
"Jiahao Pang",
"Duanshun Li",
"Dong Tian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_TearingNet_Point_Cloud_Autoencoder_To_Learn_Topology-Friendly_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_TearingNet_Point_Cloud_CVPR_2021_supplemental.pdf | 2006.10187 | cvf | @InProceedings{Pang_2021_CVPR,
author = {Pang, Jiahao and Li, Duanshun and Tian, Dong},
title = {TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoenco... |
Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper | Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics | [
"Yanzhao Wu",
"Ling Liu",
"Zhongwei Xie",
"Ka-Ho Chow",
"Wenqi Wei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Boosting_Ensemble_Accuracy_by_Revisiting_Ensemble_Diversity_Metrics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Boosting_Ensemble_Accuracy_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wu_2021_CVPR,
author = {Wu, Yanzhao and Liu, Ling and Xie, Zhongwei and Chow, Ka-Ho and Wei, Wenqi},
title = {Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting the overall ensemble accuracy. This paper provides an in-depth study on how to design and compute ensemble... |
Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper | WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition | [
"Zheng Zhu",
"Guan Huang",
"Jiankang Deng",
"Yun Ye",
"Junjie Huang",
"Xinze Chen",
"Jiagang Zhu",
"Tian Yang",
"Jiwen Lu",
"Dalong Du",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.pdf | null | 2103.04098 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Zheng and Huang, Guan and Deng, Jiankang and Ye, Yun and Huang, Junjie and Chen, Xinze and Zhu, Jiagang and Yang, Tian and Lu, Jiwen and Du, Dalong and Zhou, Jie},
title = {WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition},
... | In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name list and download 260M faces from t... |
Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper | RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection | [
"Pei Sun",
"Weiyue Wang",
"Yuning Chai",
"Gamaleldin Elsayed",
"Alex Bewley",
"Xiao Zhang",
"Cristian Sminchisescu",
"Dragomir Anguelov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_RSN_Range_Sparse_CVPR_2021_supplemental.pdf | 2106.13365 | title_snapshot | @InProceedings{Sun_2021_CVPR,
author = {Sun, Pei and Wang, Weiyue and Chai, Yuning and Elsayed, Gamaleldin and Bewley, Alex and Zhang, Xiao and Sminchisescu, Cristian and Anguelov, Dragomir},
title = {RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection},
booktitle = {Proceedings o... | The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by new LiDARs. These larger detection ranges require more efficient and accurate detection models. Towards this goal, we propose Range Sparse ... |
Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper | Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting | [
"K. Ram Prabhakar",
"Gowtham Senthil",
"Susmit Agrawal",
"R. Venkatesh Babu",
"Rama Krishna Sai S Gorthi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Prabhakar_Labeled_From_Unlabeled_Exploiting_Unlabeled_Data_for_Few-Shot_Deep_HDR_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Prabhakar_2021_CVPR,
author = {Prabhakar, K. Ram and Senthil, Gowtham and Agrawal, Susmit and Babu, R. Venkatesh and Gorthi, Rama Krishna Sai S},
title = {Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting},
booktitle = {Proceedings of the IEEE/CVF Confe... | High Dynamic Range (HDR) deghosting is an indispensable tool in capturing wide dynamic range scenes without ghosting artifacts. Recently, convolutional neural networks (CNNs) have shown tremendous success in HDR deghosting. However, CNN-based HDR deghosting methods require collecting large datasets with ground truth, w... |
Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper | Convolutional Dynamic Alignment Networks for Interpretable Classifications | [
"Moritz Bohle",
"Mario Fritz",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bohle_Convolutional_Dynamic_Alignment_Networks_for_Interpretable_Classifications_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bohle_Convolutional_Dynamic_Alignment_CVPR_2021_supplemental.pdf | 2104.00032 | cvf | @InProceedings{Bohle_2021_CVPR,
author = {Bohle, Moritz and Fritz, Mario and Schiele, Bernt},
title = {Convolutional Dynamic Alignment Networks for Interpretable Classifications},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which linearly transform their input with weight vectors that d... |
Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper | EDNet: Efficient Disparity Estimation With Cost Volume Combination and Attention-Based Spatial Residual | [
"Songyan Zhang",
"Zhicheng Wang",
"Qiang Wang",
"Jinshuo Zhang",
"Gang Wei",
"Xiaowen Chu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_EDNet_Efficient_Disparity_Estimation_With_Cost_Volume_Combination_and_Attention-Based_CVPR_2021_paper.pdf | null | 2010.13338 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Songyan and Wang, Zhicheng and Wang, Qiang and Zhang, Jinshuo and Wei, Gang and Chu, Xiaowen},
title = {EDNet: Efficient Disparity Estimation With Cost Volume Combination and Attention-Based Spatial Residual},
booktitle = {Proceedings of the IEEE/CVF C... | Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network (CNN) for disparity regression, which is inefficient due to the high memory consumption and slow inference speed. In this paper, we propose a network named EDNet for e... |
Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper | Unsupervised Visual Representation Learning by Tracking Patches in Video | [
"Guangting Wang",
"Yizhou Zhou",
"Chong Luo",
"Wenxuan Xie",
"Wenjun Zeng",
"Zhiwei Xiong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Visual_Representation_Learning_by_Tracking_Patches_in_Video_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Visual_Representation_CVPR_2021_supplemental.pdf | 2105.02545 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Guangting and Zhou, Yizhou and Luo, Chong and Xie, Wenxuan and Zeng, Wenjun and Xiong, Zhiwei},
title = {Unsupervised Visual Representation Learning by Tracking Patches in Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN mod... |
Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper | Wasserstein Contrastive Representation Distillation | [
"Liqun Chen",
"Dong Wang",
"Zhe Gan",
"Jingjing Liu",
"Ricardo Henao",
"Lawrence Carin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Wasserstein_Contrastive_Representation_Distillation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Wasserstein_Contrastive_Representation_CVPR_2021_supplemental.pdf | 2012.08674 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Liqun and Wang, Dong and Gan, Zhe and Liu, Jingjing and Henao, Ricardo and Carin, Lawrence},
title = {Wasserstein Contrastive Representation Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowle... |
Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper | Learnable Companding Quantization for Accurate Low-Bit Neural Networks | [
"Kohei Yamamoto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yamamoto_Learnable_Companding_Quantization_for_Accurate_Low-Bit_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yamamoto_Learnable_Companding_Quantization_CVPR_2021_supplemental.pdf | 2103.07156 | cvf | @InProceedings{Yamamoto_2021_CVPR,
author = {Yamamoto, Kohei},
title = {Learnable Companding Quantization for Accurate Low-Bit Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Quantizing deep neural networks is an effective method for reducing memory consumption and improving inference speed, and is thus useful for implementation in resource-constrained devices. However, it is still hard for extremely low-bit models to achieve accuracy comparable with that of full-precision models. To addres... |
Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper | FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains | [
"Jia Li",
"Zhaoyang Li",
"Jie Cao",
"Xingguang Song",
"Ran He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_FaceInpainter_High_Fidelity_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Jia and Li, Zhaoyang and Cao, Jie and Song, Xingguang and He, Ran},
title = {FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed backg... |
Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper | How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? | [
"Akshay Mehra",
"Bhavya Kailkhura",
"Pin-Yu Chen",
"Jihun Hamm"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mehra_How_Robust_Are_Randomized_Smoothing_Based_Defenses_to_Data_Poisoning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mehra_How_Robust_Are_CVPR_2021_supplemental.pdf | 2012.01274 | cvf | @InProceedings{Mehra_2021_CVPR,
author = {Mehra, Akshay and Kailkhura, Bhavya and Chen, Pin-Yu and Hamm, Jihun},
title = {How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality in achieving high c... |
Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper | Deep Learning in Latent Space for Video Prediction and Compression | [
"Bowen Liu",
"Yu Chen",
"Shiyu Liu",
"Hun-Seok Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Deep_Learning_in_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Bowen and Chen, Yu and Liu, Shiyu and Kim, Hun-Seok},
title = {Deep Learning in Latent Space for Video Prediction and Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Learning-based video compression has achieved substantial progress during recent years. The most influential approaches adopt deep neural networks (DNNs) to remove spatial and temporal redundancies by finding the appropriate lower-dimensional representations of frames in the video. We propose a novel DNN based framewor... |
Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper | PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization | [
"Guangming Wang",
"Xinrui Wu",
"Zhe Liu",
"Hesheng Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_PWCLO-Net_Deep_LiDAR_CVPR_2021_supplemental.zip | 2012.00972 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Guangming and Wu, Xinrui and Liu, Zhe and Wang, Hesheng},
title = {PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hi... |
Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper | ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning | [
"Liyuan Wang",
"Kuo Yang",
"Chongxuan Li",
"Lanqing Hong",
"Zhenguo Li",
"Jun Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_ORDisCo_Effective_and_Efficient_Usage_of_Incremental_Unlabeled_Data_for_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_ORDisCo_Effective_and_CVPR_2021_supplemental.pdf | 2101.00407 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Liyuan and Yang, Kuo and Li, Chongxuan and Hong, Lanqing and Li, Zhenguo and Zhu, Jun},
title = {ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Confer... | Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to co... |
Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper | Dynamic Region-Aware Convolution | [
"Jin Chen",
"Xijun Wang",
"Zichao Guo",
"Xiangyu Zhang",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Dynamic_Region-Aware_Convolution_CVPR_2021_supplemental.pdf | 2003.12243 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Jin and Wang, Xijun and Guo, Zichao and Zhang, Xiangyu and Sun, Jian},
title = {Dynamic Region-Aware Convolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We propose a new convolution called Dynamic Region-Aware Convolution (DRConv), which can automatically assign multiple filters to corresponding spatial regions where features have similar representation. In this way, DRConv outperforms standard convolution in modeling semantic variations. Standard convolutional layer c... |
Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper | Explore Image Deblurring via Encoded Blur Kernel Space | [
"Phong Tran",
"Anh Tuan Tran",
"Quynh Phung",
"Minh Hoai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tran_Explore_Image_Deblurring_CVPR_2021_supplemental.pdf | 2104.00317 | title_judge | @InProceedings{Tran_2021_CVPR,
author = {Tran, Phong and Tran, Anh Tuan and Phung, Quynh and Hoai, Minh},
title = {Explore Image Deblurring via Encoded Blur Kernel Space},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen ... |
Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper | BCNet: Searching for Network Width With Bilaterally Coupled Network | [
"Xiu Su",
"Shan You",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_BCNet_Searching_for_Network_Width_With_Bilaterally_Coupled_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Su_BCNet_Searching_for_CVPR_2021_supplemental.pdf | 2105.10533 | cvf | @InProceedings{Su_2021_CVPR,
author = {Su, Xiu and You, Shan and Wang, Fei and Qian, Chen and Zhang, Changshui and Xu, Chang},
title = {BCNet: Searching for Network Width With Bilaterally Coupled Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance \wrt different network width... |
Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper | Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias | [
"Yunhan Zhao",
"Shu Kong",
"Charless Fowlkes"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Camera_Pose_Matters_Improving_Depth_Prediction_by_Mitigating_Pose_Distribution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Camera_Pose_Matters_CVPR_2021_supplemental.pdf | 2007.03887 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Yunhan and Kong, Shu and Fowlkes, Charless},
title = {Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fail to make reliable depth predictions for testing examples captured under uncommon camera poses. To address this issue, we propose two novel ... |
Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper | Lipstick Ain't Enough: Beyond Color Matching for In-the-Wild Makeup Transfer | [
"Thao Nguyen",
"Anh Tuan Tran",
"Minh Hoai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_Lipstick_Aint_Enough_CVPR_2021_supplemental.pdf | 2104.01867 | title_snapshot | @InProceedings{Nguyen_2021_CVPR,
author = {Nguyen, Thao and Tran, Anh Tuan and Hoai, Minh},
title = {Lipstick Ain't Enough: Beyond Color Matching for In-the-Wild Makeup Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer... |
Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper | Generative Interventions for Causal Learning | [
"Chengzhi Mao",
"Augustine Cha",
"Amogh Gupta",
"Hao Wang",
"Junfeng Yang",
"Carl Vondrick"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mao_Generative_Interventions_for_Causal_Learning_CVPR_2021_paper.pdf | null | 2012.12265 | cvf | @InProceedings{Mao_2021_CVPR,
author = {Mao, Chengzhi and Cha, Augustine and Gupta, Amogh and Wang, Hao and Yang, Junfeng and Vondrick, Carl},
title = {Generative Interventions for Causal Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can... |
Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper | Graph Stacked Hourglass Networks for 3D Human Pose Estimation | [
"Tianhan Xu",
"Wataru Takano"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Graph_Stacked_Hourglass_Networks_for_3D_Human_Pose_Estimation_CVPR_2021_paper.pdf | null | 2103.16385 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Tianhan and Takano, Wataru},
title = {Graph Stacked Hourglass Networks for 3D Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal represe... |
Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper | Adaptive Aggregation Networks for Class-Incremental Learning | [
"Yaoyao Liu",
"Bernt Schiele",
"Qianru Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Adaptive_Aggregation_Networks_for_Class-Incremental_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Adaptive_Aggregation_Networks_CVPR_2021_supplemental.pdf | 2010.05063 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru},
title = {Adaptive Aggregation Networks for Class-Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are we... |
Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper | VS-Net: Voting With Segmentation for Visual Localization | [
"Zhaoyang Huang",
"Han Zhou",
"Yijin Li",
"Bangbang Yang",
"Yan Xu",
"Xiaowei Zhou",
"Hujun Bao",
"Guofeng Zhang",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_VS-Net_Voting_With_Segmentation_for_Visual_Localization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_VS-Net_Voting_With_CVPR_2021_supplemental.pdf | 2105.10886 | title_snapshot | @InProceedings{Huang_2021_CVPR,
author = {Huang, Zhaoyang and Zhou, Han and Li, Yijin and Yang, Bangbang and Xu, Yan and Zhou, Xiaowei and Bao, Hujun and Zhang, Guofeng and Li, Hongsheng},
title = {VS-Net: Voting With Segmentation for Visual Localization},
booktitle = {Proceedings of the IEEE/CVF Con... | Visual localization is of great importance in robotics and computer vision. Recently, scene coordinate regression based methods have shown good performance in visual localization in small static scenes. However, it still estimates camera poses from many inferior scene coordinates. To address this problem, we propose a ... |
Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper | Learning To Identify Correct 2D-2D Line Correspondences on Sphere | [
"Haoang Li",
"Kai Chen",
"Ji Zhao",
"Jiangliu Wang",
"Pyojin Kim",
"Zhe Liu",
"Yun-Hui Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_To_Identify_Correct_2D-2D_Line_Correspondences_on_Sphere_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Haoang and Chen, Kai and Zhao, Ji and Wang, Jiangliu and Kim, Pyojin and Liu, Zhe and Liu, Yun-Hui},
title = {Learning To Identify Correct 2D-2D Line Correspondences on Sphere},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Given a set of putative 2D-2D line correspondences, we aim to identify correct matches. Existing methods exploit the geometric constraints. They are only applicable to structured scenes with orthogonality, parallelism and coplanarity. In contrast, we propose the first approach suitable for both structured and unstructu... |
Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper | Domain-Independent Dominance of Adaptive Methods | [
"Pedro Savarese",
"David McAllester",
"Sudarshan Babu",
"Michael Maire"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Savarese_Domain-Independent_Dominance_of_Adaptive_Methods_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Savarese_Domain-Independent_Dominance_of_CVPR_2021_supplemental.pdf | 1912.01823 | cvf | @InProceedings{Savarese_2021_CVPR,
author = {Savarese, Pedro and McAllester, David and Babu, Sudarshan and Maire, Michael},
title = {Domain-Independent Dominance of Adaptive Methods},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. I... |
Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper | What if We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels | [
"Jeonghun Baek",
"Yusuke Matsui",
"Kiyoharu Aizawa"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Baek_What_if_We_CVPR_2021_supplemental.pdf | 2103.04400 | cvf | @InProceedings{Baek_2021_CVPR,
author = {Baek, Jeonghun and Matsui, Yusuke and Aizawa, Kiyoharu},
title = {What if We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artis... |
Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper | Incremental Learning via Rate Reduction | [
"Ziyang Wu",
"Christina Baek",
"Chong You",
"Yi Ma"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Incremental_Learning_via_Rate_Reduction_CVPR_2021_paper.pdf | null | 2011.14593 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Ziyang and Baek, Christina and You, Chong and Ma, Yi},
title = {Incremental Learning via Rate Reduction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The fundamental roadblock faced by deep learning methods is that the models are optimized as "black boxes", making it difficult to properly adju... |
Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper | Neural Descent for Visual 3D Human Pose and Shape | [
"Andrei Zanfir",
"Eduard Gabriel Bazavan",
"Mihai Zanfir",
"William T. Freeman",
"Rahul Sukthankar",
"Cristian Sminchisescu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zanfir_Neural_Descent_for_CVPR_2021_supplemental.zip | 2008.06910 | cvf | @InProceedings{Zanfir_2021_CVPR,
author = {Zanfir, Andrei and Bazavan, Eduard Gabriel and Zanfir, Mihai and Freeman, William T. and Sukthankar, Rahul and Sminchisescu, Cristian},
title = {Neural Descent for Visual 3D Human Pose and Shape},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | We present deep neural network methodology to reconstruct the 3d pose and shape of people, including hand gestures and facial expression, given an input RGB image. We rely on a recently introduced, expressive full body statistical 3d human model, GHUM, trained end-to-end, and learn to reconstruct its pose and shape sta... |
Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper | HR-NAS: Searching Efficient High-Resolution Neural Architectures With Lightweight Transformers | [
"Mingyu Ding",
"Xiaochen Lian",
"Linjie Yang",
"Peng Wang",
"Xiaojie Jin",
"Zhiwu Lu",
"Ping Luo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_HR-NAS_Searching_Efficient_High-Resolution_Neural_Architectures_With_Lightweight_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ding_HR-NAS_Searching_Efficient_CVPR_2021_supplemental.pdf | 2106.06560 | title_snapshot | @InProceedings{Ding_2021_CVPR,
author = {Ding, Mingyu and Lian, Xiaochen and Yang, Linjie and Wang, Peng and Jin, Xiaojie and Lu, Zhiwu and Luo, Ping},
title = {HR-NAS: Searching Efficient High-Resolution Neural Architectures With Lightweight Transformers},
booktitle = {Proceedings of the IEEE/CVF Co... | High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-N... |
Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper | Transitional Adaptation of Pretrained Models for Visual Storytelling | [
"Youngjae Yu",
"Jiwan Chung",
"Heeseung Yun",
"Jongseok Kim",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Transitional_Adaptation_of_Pretrained_Models_for_Visual_Storytelling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Transitional_Adaptation_of_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yu_2021_CVPR,
author = {Yu, Youngjae and Chung, Jiwan and Yun, Heeseung and Kim, Jongseok and Kim, Gunhee},
title = {Transitional Adaptation of Pretrained Models for Visual Storytelling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are o... |
Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper | Improving Panoptic Segmentation at All Scales | [
"Lorenzo Porzi",
"Samuel Rota Bulo",
"Peter Kontschieder"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Porzi_Improving_Panoptic_Segmentation_at_All_Scales_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Porzi_Improving_Panoptic_Segmentation_CVPR_2021_supplemental.pdf | 2012.07717 | cvf | @InProceedings{Porzi_2021_CVPR,
author = {Porzi, Lorenzo and Bulo, Samuel Rota and Kontschieder, Peter},
title = {Improving Panoptic Segmentation at All Scales},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Crop-based training strategies decouple training resolution from GPU memory consumption, allowing the use of large-capacity panoptic segmentation networks on multi-megapixel images. Using crops, however, can introduce a bias towards truncating or missing large objects. To address this, we propose a novel crop-aware bou... |
Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper | Model-Contrastive Federated Learning | [
"Qinbin Li",
"Bingsheng He",
"Dawn Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Model-Contrastive_Federated_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Model-Contrastive_Federated_Learning_CVPR_2021_supplemental.pdf | 2103.16257 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Qinbin and He, Bingsheng and Song, Dawn},
title = {Model-Contrastive Federated Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = ... | Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we fin... |
Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper | Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? | [
"Ruoxi Jia",
"Fan Wu",
"Xuehui Sun",
"Jiacen Xu",
"David Dao",
"Bhavya Kailkhura",
"Ce Zhang",
"Bo Li",
"Dawn Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_Scalability_vs._Utility_Do_We_Have_To_Sacrifice_One_for_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jia_Scalability_vs._Utility_CVPR_2021_supplemental.pdf | 1911.07128 | cvf | @InProceedings{Jia_2021_CVPR,
author = {Jia, Ruoxi and Wu, Fan and Sun, Xuehui and Xu, Jiacen and Dao, David and Kailkhura, Bhavya and Zhang, Ce and Li, Bo and Song, Dawn},
title = {Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification?},
booktitle = {Pr... | Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out error of each training ... |
She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper | Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment | [
"Dongyu She",
"Yu-Kun Lai",
"Gaoxiong Yi",
"Kun Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/She_Hierarchical_Layout-Aware_Graph_CVPR_2021_supplemental.pdf | null | null | @InProceedings{She_2021_CVPR,
author = {She, Dongyu and Lai, Yu-Kun and Yi, Gaoxiong and Xu, Kun},
title = {Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. W... |
Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper | Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement | [
"Huiwen Luo",
"Koki Nagano",
"Han-Wei Kung",
"Qingguo Xu",
"Zejian Wang",
"Lingyu Wei",
"Liwen Hu",
"Hao Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Normalized_Avatar_Synthesis_Using_StyleGAN_and_Perceptual_Refinement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Normalized_Avatar_Synthesis_CVPR_2021_supplemental.zip | 2106.11423 | cvf | @InProceedings{Luo_2021_CVPR,
author = {Luo, Huiwen and Nagano, Koki and Kung, Han-Wei and Xu, Qingguo and Wang, Zejian and Wei, Lingyu and Hu, Liwen and Li, Hao},
title = {Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person's face in neutral expr... |
Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper | CT-Net: Complementary Transfering Network for Garment Transfer With Arbitrary Geometric Changes | [
"Fan Yang",
"Guosheng Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_CT-Net_Complementary_Transfering_Network_for_Garment_Transfer_With_Arbitrary_Geometric_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_CT-Net_Complementary_Transfering_CVPR_2021_supplemental.pdf | 2105.05497 | title_snapshot | @InProceedings{Yang_2021_CVPR,
author = {Yang, Fan and Lin, Guosheng},
title = {CT-Net: Complementary Transfering Network for Garment Transfer With Arbitrary Geometric Changes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) t... |
Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper | MetaCorrection: Domain-Aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation | [
"Xiaoqing Guo",
"Chen Yang",
"Baopu Li",
"Yixuan Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.pdf | null | 2103.05254 | cvf | @InProceedings{Guo_2021_CVPR,
author = {Guo, Xiaoqing and Yang, Chen and Li, Baopu and Yuan, Yixuan},
title = {MetaCorrection: Domain-Aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However,... |
Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper | Multi-Stage Progressive Image Restoration | [
"Syed Waqas Zamir",
"Aditya Arora",
"Salman Khan",
"Munawar Hayat",
"Fahad Shahbaz Khan",
"Ming-Hsuan Yang",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zamir_Multi-Stage_Progressive_Image_Restoration_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zamir_Multi-Stage_Progressive_Image_CVPR_2021_supplemental.pdf | 2102.02808 | cvf | @InProceedings{Zamir_2021_CVPR,
author = {Zamir, Syed Waqas and Arora, Aditya and Khan, Salman and Hayat, Munawar and Khan, Fahad Shahbaz and Yang, Ming-Hsuan and Shao, Ling},
title = {Multi-Stage Progressive Image Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns re... |
Li_PointNetLK_Revisited_CVPR_2021_paper | PointNetLK Revisited | [
"Xueqian Li",
"Jhony Kaesemodel Pontes",
"Simon Lucey"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_PointNetLK_Revisited_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_PointNetLK_Revisited_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_PointNetLK_Revisited_CVPR_2021_supplemental.pdf | 2008.09527 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Xueqian and Pontes, Jhony Kaesemodel and Lucey, Simon},
title = {PointNetLK Revisited},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1... | We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or u... |
Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper | Deep Convolutional Dictionary Learning for Image Denoising | [
"Hongyi Zheng",
"Hongwei Yong",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Convolutional_Dictionary_Learning_for_Image_Denoising_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Deep_Convolutional_Dictionary_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Hongyi and Yong, Hongwei and Zhang, Lei},
title = {Deep Convolutional Dictionary Learning for Image Denoising},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Inspired by the great success of deep neural networks (DNNs), many unfolding methods have been proposed to integrate traditional image modeling techniques, such as dictionary learning (DicL) and sparse coding, into DNNs for image restoration. However, the performance of such methods remains limited for several reasons.... |
Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper | Fourier Contour Embedding for Arbitrary-Shaped Text Detection | [
"Yiqin Zhu",
"Jianyong Chen",
"Lingyu Liang",
"Zhanghui Kuang",
"Lianwen Jin",
"Wayne Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.pdf | null | 2104.10442 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Yiqin and Chen, Jianyong and Liang, Lingyu and Kuang, Zhanghui and Jin, Lianwen and Zhang, Wayne},
title = {Fourier Contour Embedding for Arbitrary-Shaped Text Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordina... |
Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper | TAP: Text-Aware Pre-Training for Text-VQA and Text-Caption | [
"Zhengyuan Yang",
"Yijuan Lu",
"Jianfeng Wang",
"Xi Yin",
"Dinei Florencio",
"Lijuan Wang",
"Cha Zhang",
"Lei Zhang",
"Jiebo Luo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_TAP_Text-Aware_Pre-Training_for_Text-VQA_and_Text-Caption_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_TAP_Text-Aware_Pre-Training_CVPR_2021_supplemental.pdf | 2012.04638 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Zhengyuan and Lu, Yijuan and Wang, Jianfeng and Yin, Xi and Florencio, Dinei and Wang, Lijuan and Zhang, Cha and Zhang, Lei and Luo, Jiebo},
title = {TAP: Text-Aware Pre-Training for Text-VQA and Text-Caption},
booktitle = {Proceedings of the IEEE/CVF Co... | In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answering and image caption generation, respectively. In contrast to the conventional vision-language pre-training that fails to capture scene t... |
Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper | Seeing Out of the Box: End-to-End Pre-Training for Vision-Language Representation Learning | [
"Zhicheng Huang",
"Zhaoyang Zeng",
"Yupan Huang",
"Bei Liu",
"Dongmei Fu",
"Jianlong Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Seeing_Out_of_CVPR_2021_supplemental.pdf | 2104.03135 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Zhicheng and Zeng, Zhaoyang and Huang, Yupan and Liu, Bei and Fu, Dongmei and Fu, Jianlong},
title = {Seeing Out of the Box: End-to-End Pre-Training for Vision-Language Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | We study on joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based repr... |
Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper | Quality-Agnostic Image Recognition via Invertible Decoder | [
"Insoo Kim",
"Seungju Han",
"Ji-won Baek",
"Seong-Jin Park",
"Jae-Joon Han",
"Jinwoo Shin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Quality-Agnostic_Image_Recognition_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Kim_2021_CVPR,
author = {Kim, Insoo and Han, Seungju and Baek, Ji-won and Park, Seong-Jin and Han, Jae-Joon and Shin, Jinwoo},
title = {Quality-Agnostic Image Recognition via Invertible Decoder},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Despite the remarkable performance of deep models on image recognition tasks, they are known to be susceptible to common corruptions such as blur, noise, and low-resolution. Data augmentation is a conventional way to build a robust model by considering these common corruptions during the training. However, a naive data... |
Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper | Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach | [
"Yu Chen",
"Ji Zhao",
"Laurent Kneip"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Hybrid_Rotation_Averaging_A_Fast_and_Robust_Rotation_Averaging_Approach_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Hybrid_Rotation_Averaging_CVPR_2021_supplemental.pdf | 2101.09116 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yu and Zhao, Ji and Kneip, Laurent},
title = {Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the i... |
Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper | One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation | [
"Zhengzhe Liu",
"Xiaojuan Qi",
"Chi-Wing Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_One_Thing_One_Click_A_Self-Training_Approach_for_Weakly_Supervised_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_One_Thing_One_CVPR_2021_supplemental.pdf | 2104.02246 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Zhengzhe and Qi, Xiaojuan and Fu, Chi-Wing},
title = {One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose "One Thing One Click," meaning th... |
Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper | Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces | [
"Alireza Zaeemzadeh",
"Niccolo Bisagno",
"Zeno Sambugaro",
"Nicola Conci",
"Nazanin Rahnavard",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zaeemzadeh_Out-of-Distribution_Detection_Using_Union_of_1-Dimensional_Subspaces_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zaeemzadeh_Out-of-Distribution_Detection_Using_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zaeemzadeh_2021_CVPR,
author = {Zaeemzadeh, Alireza and Bisagno, Niccolo and Sambugaro, Zeno and Conci, Nicola and Rahnavard, Nazanin and Shah, Mubarak},
title = {Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces},
booktitle = {Proceedings of the IEEE/CVF Conference ... | The goal of out-of-distribution (OOD) detection is to handle the situations where the test samples are drawn from a different distribution than the training data. In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded tr... |
Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper | MP3: A Unified Model To Map, Perceive, Predict and Plan | [
"Sergio Casas",
"Abbas Sadat",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Casas_MP3_A_Unified_Model_To_Map_Perceive_Predict_and_Plan_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Casas_MP3_A_Unified_CVPR_2021_supplemental.zip | 2101.06806 | cvf | @InProceedings{Casas_2021_CVPR,
author = {Casas, Sergio and Sadat, Abbas and Urtasun, Raquel},
title = {MP3: A Unified Model To Map, Perceive, Predict and Plan},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with ... |
Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper | SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements | [
"Qianli Ma",
"Shunsuke Saito",
"Jinlong Yang",
"Siyu Tang",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_SCALE_Modeling_Clothed_Humans_with_a_Surface_Codec_of_Articulated_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_SCALE_Modeling_Clothed_CVPR_2021_supplemental.pdf | 2104.07660 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Qianli and Saito, Shunsuke and Yang, Jinlong and Tang, Siyu and Black, Michael J.},
title = {SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the choice of representation is the key. Recent work uses neural networks to parameterize local surface elements. This approach captures locally ... |
Menapace_Playable_Video_Generation_CVPR_2021_paper | Playable Video Generation | [
"Willi Menapace",
"Stephane Lathuiliere",
"Sergey Tulyakov",
"Aliaksandr Siarohin",
"Elisa Ricci"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Menapace_Playable_Video_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Menapace_Playable_Video_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Menapace_Playable_Video_Generation_CVPR_2021_supplemental.pdf | 2101.12195 | cvf | @InProceedings{Menapace_2021_CVPR,
author = {Menapace, Willi and Lathuiliere, Stephane and Tulyakov, Sergey and Siarohin, Aliaksandr and Ricci, Elisa},
title = {Playable Video Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions an... |
Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper | AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations From Self-Trained Negative Adversaries | [
"Qianjiang Hu",
"Xiao Wang",
"Wei Hu",
"Guo-Jun Qi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_AdCo_Adversarial_Contrast_for_Efficient_Learning_of_Unsupervised_Representations_From_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_AdCo_Adversarial_Contrast_CVPR_2021_supplemental.pdf | 2011.08435 | cvf | @InProceedings{Hu_2021_CVPR,
author = {Hu, Qianjiang and Wang, Xiao and Hu, Wei and Qi, Guo-Jun},
title = {AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations From Self-Trained Negative Adversaries},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over mini-batches while only a small portion of... |
Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper | Permute, Quantize, and Fine-Tune: Efficient Compression of Neural Networks | [
"Julieta Martinez",
"Jashan Shewakramani",
"Ting Wei Liu",
"Ioan Andrei Barsan",
"Wenyuan Zeng",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Martinez_Permute_Quantize_and_Fine-Tune_Efficient_Compression_of_Neural_Networks_CVPR_2021_paper.pdf | null | 2010.15703 | title_snapshot | @InProceedings{Martinez_2021_CVPR,
author = {Martinez, Julieta and Shewakramani, Jashan and Liu, Ting Wei and Barsan, Ioan Andrei and Zeng, Wenyuan and Urtasun, Raquel},
title = {Permute, Quantize, and Fine-Tune: Efficient Compression of Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conf... | Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single code, and has recently achieved state-of-the-art network compression on a range of... |
Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper | Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis | [
"Karren Yang",
"Samuel Goldman",
"Wengong Jin",
"Alex X. Lu",
"Regina Barzilay",
"Tommi Jaakkola",
"Caroline Uhler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Mol2Image_Improved_Conditional_Flow_Models_for_Molecule_to_Image_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Mol2Image_Improved_Conditional_CVPR_2021_supplemental.pdf | 2006.08532 | title_judge | @InProceedings{Yang_2021_CVPR,
author = {Yang, Karren and Goldman, Samuel and Jin, Wengong and Lu, Alex X. and Barzilay, Regina and Jaakkola, Tommi and Uhler, Caroline},
title = {Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Con... | In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-... |
Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper | Improved Handling of Motion Blur in Online Object Detection | [
"Mohamed Sayed",
"Gabriel Brostow"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sayed_Improved_Handling_of_Motion_Blur_in_Online_Object_Detection_CVPR_2021_paper.pdf | null | 2011.14448 | cvf | @InProceedings{Sayed_2021_CVPR,
author = {Sayed, Mohamed and Brostow, Gabriel},
title = {Improved Handling of Motion Blur in Online Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}... | We wish to detect specific categories of objects, for online vision systems that will run in the real world. Object detection is already very challenging. It is even harder when the images are blurred, from the camera being in a car or a hand-held phone. Most existing efforts either focused on sharp images, with easy t... |
Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper | Multimodal Motion Prediction With Stacked Transformers | [
"Yicheng Liu",
"Jinghuai Zhang",
"Liangji Fang",
"Qinhong Jiang",
"Bolei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Multimodal_Motion_Prediction_CVPR_2021_supplemental.pdf | 2103.11624 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yicheng and Zhang, Jinghuai and Fang, Liangji and Jiang, Qinhong and Zhou, Bolei},
title = {Multimodal Motion Prediction With Stacked Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it rem... |
Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper | The Translucent Patch: A Physical and Universal Attack on Object Detectors | [
"Alon Zolfi",
"Moshe Kravchik",
"Yuval Elovici",
"Asaf Shabtai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zolfi_The_Translucent_Patch_A_Physical_and_Universal_Attack_on_Object_CVPR_2021_paper.pdf | null | 2012.12528 | cvf | @InProceedings{Zolfi_2021_CVPR,
author = {Zolfi, Alon and Kravchik, Moshe and Elovici, Yuval and Shabtai, Asaf},
title = {The Translucent Patch: A Physical and Universal Attack on Object Detectors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we pro... |
Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper | Exploit Visual Dependency Relations for Semantic Segmentation | [
"Mingyuan Liu",
"Dan Schonfeld",
"Wei Tang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Exploit_Visual_Dependency_Relations_for_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Exploit_Visual_Dependency_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Mingyuan and Schonfeld, Dan and Tang, Wei},
title = {Exploit Visual Dependency Relations for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Dependency relations among visual entities are ubiquity because both objects and scenes are highly structured. They provide prior knowledge about the real world that can help improve the generalization ability of deep learning approaches. Different from contextual reasoning which focuses on feature aggregation in the s... |
Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper | Dense Label Encoding for Boundary Discontinuity Free Rotation Detection | [
"Xue Yang",
"Liping Hou",
"Yue Zhou",
"Wentao Wang",
"Junchi Yan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Dense_Label_Encoding_for_Boundary_Discontinuity_Free_Rotation_Detection_CVPR_2021_paper.pdf | null | 2011.09670 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Xue and Hou, Liping and Zhou, Yue and Wang, Wentao and Yan, Junchi},
title = {Dense Label Encoding for Boundary Discontinuity Free Rotation Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification. The hope is to in... |
Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper | Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On | [
"Igor Santesteban",
"Nils Thuerey",
"Miguel A. Otaduy",
"Dan Casas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Santesteban_Self-Supervised_Collision_Handling_via_Generative_3D_Garment_Models_for_Virtual_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Santesteban_Self-Supervised_Collision_Handling_CVPR_2021_supplemental.zip | 2105.06462 | cvf | @InProceedings{Santesteban_2021_CVPR,
author = {Santesteban, Igor and Thuerey, Nils and Otaduy, Miguel A. and Casas, Dan},
title = {Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | We propose a new generative model for 3D garment deformations that enables us to learn, for first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at tes... |
Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper | DexYCB: A Benchmark for Capturing Hand Grasping of Objects | [
"Yu-Wei Chao",
"Wei Yang",
"Yu Xiang",
"Pavlo Molchanov",
"Ankur Handa",
"Jonathan Tremblay",
"Yashraj S. Narang",
"Karl Van Wyk",
"Umar Iqbal",
"Stan Birchfield",
"Jan Kautz",
"Dieter Fox"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chao_DexYCB_A_Benchmark_for_Capturing_Hand_Grasping_of_Objects_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chao_DexYCB_A_Benchmark_CVPR_2021_supplemental.zip | 2104.04631 | cvf | @InProceedings{Chao_2021_CVPR,
author = {Chao, Yu-Wei and Yang, Wei and Xiang, Yu and Molchanov, Pavlo and Handa, Ankur and Tremblay, Jonathan and Narang, Yashraj S. and Van Wyk, Karl and Iqbal, Umar and Birchfield, Stan and Kautz, Jan and Fox, Dieter},
title = {DexYCB: A Benchmark for Capturing Hand Gra... | We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand po... |
Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper | Prototype Completion With Primitive Knowledge for Few-Shot Learning | [
"Baoquan Zhang",
"Xutao Li",
"Yunming Ye",
"Zhichao Huang",
"Lisai Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Prototype_Completion_With_Primitive_Knowledge_for_Few-Shot_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Prototype_Completion_With_CVPR_2021_supplemental.pdf | 2009.04960 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Baoquan and Li, Xutao and Ye, Yunming and Huang, Zhichao and Zhang, Lisai},
title = {Prototype Completion With Primitive Knowledge for Few-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that ... |
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