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Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper | SMD-Nets: Stereo Mixture Density Networks | [
"Fabio Tosi",
"Yiyi Liao",
"Carolin Schmitt",
"Andreas Geiger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper.pdf | null | 2104.03866 | title_snapshot | @InProceedings{Tosi_2021_CVPR,
author = {Tosi, Fabio and Liao, Yiyi and Schmitt, Carolin and Geiger, Andreas},
title = {SMD-Nets: Stereo Mixture Density Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide cl... |
Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper | Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification | [
"Qiong Wu",
"Pingyang Dai",
"Jie Chen",
"Chia-Wen Lin",
"Yongjian Wu",
"Feiyue Huang",
"Bineng Zhong",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wu_2021_CVPR,
author = {Wu, Qiong and Dai, Pingyang and Chen, Jie and Lin, Chia-Wen and Wu, Yongjian and Huang, Feiyue and Zhong, Bineng and Ji, Rongrong},
title = {Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF ... | Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the same identity from different modalities. Existing works mainly focus on alleviating the modality discrepancy by aligning the distributions of features from different modalities. However, nuanced but discriminative information, ... |
Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper | Learning Progressive Point Embeddings for 3D Point Cloud Generation | [
"Cheng Wen",
"Baosheng Yu",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Learning_Progressive_Point_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wen_2021_CVPR,
author = {Wen, Cheng and Yu, Baosheng and Tao, Dacheng},
title = {Learning Progressive Point Embeddings for 3D Point Cloud Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clo... |
He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper | Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking | [
"Jiawei He",
"Zehao Huang",
"Naiyan Wang",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Learnable_Graph_Matching_CVPR_2021_supplemental.pdf | 2103.16178 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Jiawei and Huang, Zehao and Wang, Naiyan and Zhang, Zhaoxiang},
title = {Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Data association across frames is at the core of Multiple Object Tracking (MOT) task. This problem is usually solved by a traditional graph-based optimization or directly learned via deep learning. Despite their popularity, we find some points worth studying in current paradigm: 1) Existing methods mostly ignore the co... |
Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper | A Decomposition Model for Stereo Matching | [
"Chengtang Yao",
"Yunde Jia",
"Huijun Di",
"Pengxiang Li",
"Yuwei Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_A_Decomposition_Model_CVPR_2021_supplemental.pdf | 2104.07516 | cvf | @InProceedings{Yao_2021_CVPR,
author = {Yao, Chengtang and Jia, Yunde and Di, Huijun and Li, Pengxiang and Wu, Yuwei},
title = {A Decomposition Model for Stereo Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resoluti... |
Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper | RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union | [
"Zhidong Liang",
"Zehan Zhang",
"Ming Zhang",
"Xian Zhao",
"Shiliang Pu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Liang_2021_CVPR,
author = {Liang, Zhidong and Zhang, Zehan and Zhang, Ming and Zhao, Xian and Pu, Shiliang},
title = {RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Real-time and high-performance 3D object detection is an attractive research direction in autonomous driving. Recent studies prefer point based or voxel based convolution for achieving high performance. However, these methods suffer from the unsatisfied efficiency or complex customized convolution, making them unsuitab... |
Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper | Domain-Robust VQA With Diverse Datasets and Methods but No Target Labels | [
"Mingda Zhang",
"Tristan Maidment",
"Ahmad Diab",
"Adriana Kovashka",
"Rebecca Hwa"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper.pdf | null | 2103.15974 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Mingda and Maidment, Tristan and Diab, Ahmad and Kovashka, Adriana and Hwa, Rebecca},
title = {Domain-Robust VQA With Diverse Datasets and Methods but No Target Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts. However, similar work on domain-robust visual question answering methods is very limited. Domain adaptation for VQA differs from adaptation for object recogn... |
Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper | (AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Semantic Segmentation Network | [
"Ran Cheng",
"Ryan Razani",
"Ehsan Taghavi",
"Enxu Li",
"Bingbing Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_AF2-S3Net_Attentive_Feature_CVPR_2021_supplemental.zip | 2102.04530 | title_snapshot | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Ran and Razani, Ryan and Taghavi, Ehsan and Li, Enxu and Liu, Bingbing},
title = {(AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Semantic Segmentation Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one the essential components of environmental perception that provides semantic information of the scene. Recently, several met... |
Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper | Towards Real-World Blind Face Restoration With Generative Facial Prior | [
"Xintao Wang",
"Yu Li",
"Honglun Zhang",
"Ying Shan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.pdf | null | 2101.04061 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xintao and Li, Yu and Zhang, Honglun and Shan, Ying},
title = {Towards Real-World Blind Face Restoration With Generative Facial Prior},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. ... |
Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper | Track To Detect and Segment: An Online Multi-Object Tracker | [
"Jialian Wu",
"Jiale Cao",
"Liangchen Song",
"Yu Wang",
"Ming Yang",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.pdf | null | 2103.08808 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Jialian and Cao, Jiale and Song, Liangchen and Wang, Yu and Yang, Ming and Yuan, Junsong},
title = {Track To Detect and Segment: An Online Multi-Object Tracker},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking... |
Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper | Look Before You Speak: Visually Contextualized Utterances | [
"Paul Hongsuck Seo",
"Arsha Nagrani",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Seo_Look_Before_You_CVPR_2021_supplemental.pdf | 2012.05710 | cvf | @InProceedings{Seo_2021_CVPR,
author = {Seo, Paul Hongsuck and Nagrani, Arsha and Schmid, Cordelia},
title = {Look Before You Speak: Visually Contextualized Utterances},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We pr... |
Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper | DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network | [
"Rui Liu",
"Yixiao Ge",
"Ching Lam Choi",
"Xiaogang Wang",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper.pdf | null | 2103.07893 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Rui and Ge, Yixiao and Choi, Ching Lam and Wang, Xiaogang and Li, Hongsheng},
title = {DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. Towards solving this issue, previous works mainly focused on encouraging the correlation between the latent codes... |
Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper | Effective Sparsification of Neural Networks With Global Sparsity Constraint | [
"Xiao Zhou",
"Weizhong Zhang",
"Hang Xu",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Effective_Sparsification_of_CVPR_2021_supplemental.pdf | 2105.01571 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Xiao and Zhang, Weizhong and Xu, Hang and Zhang, Tong},
title = {Effective Sparsification of Neural Networks With Global Sparsity Constraint},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real world deployments. However, since magnitudes and relative importance of weights are very different for different layers of a neural network, existing methods rely on either manual tuning or handcrafted ... |
Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper | Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging | [
"Tao Huang",
"Weisheng Dong",
"Xin Yuan",
"Jinjian Wu",
"Guangming Shi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Deep_Gaussian_Scale_CVPR_2021_supplemental.pdf | 2103.07152 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Tao and Dong, Weisheng and Yuan, Xin and Wu, Jinjian and Shi, Guangming},
title = {Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-based HSI reconstruction methods employed hand-crafted priors to solve the reconstruction problem, but most of which achieved limited success... |
Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation | [
"Zhekai Du",
"Jingjing Li",
"Hongzu Su",
"Lei Zhu",
"Ke Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | null | 2106.04151 | cvf | @InProceedings{Du_2021_CVPR,
author = {Du, Zhekai and Li, Jingjing and Su, Hongzu and Zhu, Lei and Lu, Ke},
title = {Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (bi-classifier) has been introduced into UDA which is effective to align distributions between different dom... |
Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper | DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks | [
"Abhishek Singh",
"Ayush Chopra",
"Ethan Garza",
"Emily Zhang",
"Praneeth Vepakomma",
"Vivek Sharma",
"Ramesh Raskar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_DISCO_Dynamic_and_CVPR_2021_supplemental.pdf | 2012.11025 | cvf | @InProceedings{Singh_2021_CVPR,
author = {Singh, Abhishek and Chopra, Ayush and Garza, Ethan and Zhang, Emily and Vepakomma, Praneeth and Sharma, Vivek and Raskar, Ramesh},
title = {DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks},
booktitle = {Proceedings of the I... | Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especiall... |
Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper | Training Generative Adversarial Networks in One Stage | [
"Chengchao Shen",
"Youtan Yin",
"Xinchao Wang",
"Xubin Li",
"Jie Song",
"Mingli Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Training_Generative_Adversarial_CVPR_2021_supplemental.pdf | 2103.00430 | cvf | @InProceedings{Shen_2021_CVPR,
author = {Shen, Chengchao and Yin, Youtan and Wang, Xinchao and Li, Xubin and Song, Jie and Song, Mingli},
title = {Training Generative Adversarial Networks in One Stage},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a gene... |
Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper | Learning To Aggregate and Personalize 3D Face From In-the-Wild Photo Collection | [
"Zhenyu Zhang",
"Yanhao Ge",
"Renwang Chen",
"Ying Tai",
"Yan Yan",
"Jian Yang",
"Chengjie Wang",
"Jilin Li",
"Feiyue Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.pdf | null | 2106.07852 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Zhenyu and Ge, Yanhao and Chen, Renwang and Tai, Ying and Yan, Yan and Yang, Jian and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
title = {Learning To Aggregate and Personalize 3D Face From In-the-Wild Photo Collection},
booktitle = {Proceedings o... | Non-prior face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Personali... |
Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper | Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching | [
"Qi Jia",
"ZhengJun Li",
"Xin Fan",
"Haotian Zhao",
"Shiyu Teng",
"Xinchen Ye",
"Longin Jan Latecki"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Jia_2021_CVPR,
author = {Jia, Qi and Li, ZhengJun and Fan, Xin and Zhao, Haotian and Teng, Shiyu and Ye, Xinchen and Latecki, Longin Jan},
title = {Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching},
booktitle = {Proceedings of the IEEE/CVF Conf... | Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match... |
Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper | 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection | [
"He Wang",
"Yezhen Cong",
"Or Litany",
"Yue Gao",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_3DIoUMatch_Leveraging_IoU_CVPR_2021_supplemental.pdf | 2012.04355 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, He and Cong, Yezhen and Litany, Or and Gao, Yue and Guibas, Leonidas J.},
title = {3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | 3D object detection is an important yet demanding task that heavily relies on difficult to obtain 3D annotations. To reduce the required amount of supervision, we propose 3DIoUMatch, a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual... |
Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper | Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency | [
"Sangeek Hyun",
"Jihwan Kim",
"Jae-Pil Heo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Hyun_2021_CVPR,
author = {Hyun, Sangeek and Kim, Jihwan and Heo, Jae-Pil},
title = {Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance cons... |
Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper | Neural Lumigraph Rendering | [
"Petr Kellnhofer",
"Lars C. Jebe",
"Andrew Jones",
"Ryan Spicer",
"Kari Pulli",
"Gordon Wetzstein"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_supplemental.pdf | 2103.11571 | cvf | @InProceedings{Kellnhofer_2021_CVPR,
author = {Kellnhofer, Petr and Jebe, Lars C. and Jones, Andrew and Spicer, Ryan and Pulli, Kari and Wetzstein, Gordon},
title = {Neural Lumigraph Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural rendering techniques have recently achieved photorealistic image quality for this task. State-of-the-art (SOTA) neural volume rendering approaches, however, are slow to train and require minutes of inference (i.e., rendering) time for... |
Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper | Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals | [
"Kun Qian",
"Shilin Zhu",
"Xinyu Zhang",
"Li Erran Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qian_Robust_Multimodal_Vehicle_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Qian_2021_CVPR,
author = {Qian, Kun and Zhu, Shilin and Zhang, Xinyu and Li, Li Erran},
title = {Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort ligh... |
Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper | Stochastic Whitening Batch Normalization | [
"Shengdong Zhang",
"Ehsan Nezhadarya",
"Homa Fashandi",
"Jiayi Liu",
"Darin Graham",
"Mohak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Stochastic_Whitening_Batch_CVPR_2021_supplemental.pdf | 2106.04413 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Shengdong and Nezhadarya, Ehsan and Fashandi, Homa and Liu, Jiayi and Graham, Darin and Shah, Mohak},
title = {Stochastic Whitening Batch Normalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the... |
Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper | Self-Guided and Cross-Guided Learning for Few-Shot Segmentation | [
"Bingfeng Zhang",
"Jimin Xiao",
"Terry Qin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Self-Guided_and_Cross-Guided_CVPR_2021_supplemental.pdf | 2103.16129 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Bingfeng and Xiao, Jimin and Qin, Terry},
title = {Self-Guided and Cross-Guided Learning for Few-Shot Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Few-shot segmentation has been attracting a lot of attention due to its effectiveness to segment unseen object classes with a few annotated samples. Most existing approaches use masked Global Average Pooling (GAP) to encode an annotated support image to a feature vector to facilitate query image segmentation. However, ... |
Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper | M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-Training | [
"Minheng Ni",
"Haoyang Huang",
"Lin Su",
"Edward Cui",
"Taroon Bharti",
"Lijuan Wang",
"Dongdong Zhang",
"Nan Duan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.pdf | null | 2006.02635 | cvf | @InProceedings{Ni_2021_CVPR,
author = {Ni, Minheng and Huang, Haoyang and Su, Lin and Cui, Edward and Bharti, Taroon and Wang, Lijuan and Zhang, Dongdong and Duan, Nan},
title = {M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-Training},
booktitle = {Proceedings of t... | We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in differ... |
Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper | Hyperdimensional Computing as a Framework for Systematic Aggregation of Image Descriptors | [
"Peer Neubert",
"Stefan Schubert"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Neubert_Hyperdimensional_Computing_as_CVPR_2021_supplemental.pdf | 2101.07720 | cvf | @InProceedings{Neubert_2021_CVPR,
author = {Neubert, Peer and Schubert, Stefan},
title = {Hyperdimensional Computing as a Framework for Systematic Aggregation of Image Descriptors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for c... |
Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper | Layerwise Optimization by Gradient Decomposition for Continual Learning | [
"Shixiang Tang",
"Dapeng Chen",
"Jinguo Zhu",
"Shijie Yu",
"Wanli Ouyang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Layerwise_Optimization_by_CVPR_2021_supplemental.pdf | 2105.07561 | cvf | @InProceedings{Tang_2021_CVPR,
author = {Tang, Shixiang and Chen, Dapeng and Zhu, Jinguo and Yu, Shijie and Ouyang, Wanli},
title = {Layerwise Optimization by Gradient Decomposition for Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Deep neural networks achieve state-of-the-art and sometimes super-human performance across a variety of domains. However, when learning tasks sequentially, the networks easily forget the knowledge of previous tasks, known as "catastrophic forgetting". To achieve the consistencies between the old tasks and the new task,... |
Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper | Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging | [
"S. Mahdi H. Miangoleh",
"Sebastian Dille",
"Long Mai",
"Sylvain Paris",
"Yagiz Aksoy"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Miangoleh_Boosting_Monocular_Depth_CVPR_2021_supplemental.zip | 2105.14021 | cvf | @InProceedings{Miangoleh_2021_CVPR,
author = {Miangoleh, S. Mahdi H. and Dille, Sebastian and Mai, Long and Paris, Sylvain and Aksoy, Yagiz},
title = {Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging},
booktitle = {Proceedings of the IEEE/CVF... | Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects... |
Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper | Blind Deblurring for Saturated Images | [
"Liang Chen",
"Jiawei Zhang",
"Songnan Lin",
"Faming Fang",
"Jimmy S. Ren"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Blind_Deblurring_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Liang and Zhang, Jiawei and Lin, Songnan and Fang, Faming and Ren, Jimmy S.},
title = {Blind Deblurring for Saturated Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that saturated pixels are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding saturated pixels during the deblurring... |
Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper | Turning Frequency to Resolution: Video Super-Resolution via Event Cameras | [
"Yongcheng Jing",
"Yiding Yang",
"Xinchao Wang",
"Mingli Song",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_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 = {Turning Frequency to Resolution: Video Super-Resolution via Event Cameras},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | State-of-the-art video super-resolution (VSR) methods focus on exploiting inter- and intra-frame correlations to estimate high-resolution (HR) video frames from low-resolution (LR) ones. In this paper, we study VSR from an exotic perspective, by explicitly looking into the role of temporal frequency of video frames. Th... |
Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper | Time Adaptive Recurrent Neural Network | [
"Anil Kag",
"Venkatesh Saligrama"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kag_Time_Adaptive_Recurrent_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Kag_2021_CVPR,
author = {Kag, Anil and Saligrama, Venkatesh},
title = {Time Adaptive Recurrent Neural Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {15149... | We propose a learning method that, dynamically modifies the time-constants of the continuous-time counterpart of a vanilla RNN. The time-constants are modified based on the current observation and hidden state. Our proposal overcomes the issues of RNN trainability, by mitigating exploding and vanishing gradient phenome... |
Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper | DeFMO: Deblurring and Shape Recovery of Fast Moving Objects | [
"Denys Rozumnyi",
"Martin R. Oswald",
"Vittorio Ferrari",
"Jiri Matas",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rozumnyi_DeFMO_Deblurring_and_CVPR_2021_supplemental.zip | 2012.00595 | cvf | @InProceedings{Rozumnyi_2021_CVPR,
author = {Rozumnyi, Denys and Oswald, Martin R. and Ferrari, Vittorio and Matas, Jiri and Pollefeys, Marc},
title = {DeFMO: Deblurring and Shape Recovery of Fast Moving Objects},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a ... |
Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper | PISE: Person Image Synthesis and Editing With Decoupled GAN | [
"Jinsong Zhang",
"Kun Li",
"Yu-Kun Lai",
"Jingyu Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_PISE_Person_Image_CVPR_2021_supplemental.pdf | 2103.04023 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Jinsong and Li, Kun and Lai, Yu-Kun and Yang, Jingyu},
title = {PISE: Person Image Synthesis and Editing With Decoupled GAN},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose ... |
Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper | 4D Hyperspectral Photoacoustic Data Restoration With Reliability Analysis | [
"Weihang Liao",
"Art Subpa-asa",
"Yinqiang Zheng",
"Imari Sato"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liao_4D_Hyperspectral_Photoacoustic_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liao_2021_CVPR,
author = {Liao, Weihang and Subpa-asa, Art and Zheng, Yinqiang and Sato, Imari},
title = {4D Hyperspectral Photoacoustic Data Restoration With Reliability Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains; and the acquired data tend to su... |
Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper | OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning | [
"Spyros Gidaris",
"Andrei Bursuc",
"Gilles Puy",
"Nikos Komodakis",
"Matthieu Cord",
"Patrick Perez"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gidaris_OBoW_Online_Bag-of-Visual-Words_CVPR_2021_supplemental.pdf | 2012.11552 | title_snapshot | @InProceedings{Gidaris_2021_CVPR,
author = {Gidaris, Spyros and Bursuc, Andrei and Puy, Gilles and Komodakis, Nikos and Cord, Matthieu and Perez, Patrick},
title = {OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although... |
Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper | Learning-Based Image Registration With Meta-Regularization | [
"Ebrahim Al Safadi",
"Xubo Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Al_Safadi_2021_CVPR,
author = {Al Safadi, Ebrahim and Song, Xubo},
title = {Learning-Based Image Registration With Meta-Regularization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | We introduce a meta-regularization framework for learning-based image registration. Current learning-based image registration methods use high-resolution architectures such as U-Nets to produce spatial transformations, and impose simple and explicit regularization on the output of the network to ensure that the estimat... |
Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper | A Hyperbolic-to-Hyperbolic Graph Convolutional Network | [
"Jindou Dai",
"Yuwei Wu",
"Zhi Gao",
"Yunde Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_A_Hyperbolic-to-Hyperbolic_Graph_CVPR_2021_supplemental.pdf | 2104.06942 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Jindou and Wu, Yuwei and Gao, Zhi and Jia, Yunde},
title = {A Hyperbolic-to-Hyperbolic Graph Convolutional Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is inferior because tangent space is only a local approximation of a manif... |
Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper | Deep Homography for Efficient Stereo Image Compression | [
"Xin Deng",
"Wenzhe Yang",
"Ren Yang",
"Mai Xu",
"Enpeng Liu",
"Qianhan Feng",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Deng_2021_CVPR,
author = {Deng, Xin and Yang, Wenzhe and Yang, Ren and Xu, Mai and Liu, Enpeng and Feng, Qianhan and Timofte, Radu},
title = {Deep Homography for Efficient Stereo Image Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | In this paper, we propose HESIC, an end-to-end trainable deep network for stereo image compression (SIC). To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i.e., H matrix. Then, the left image is spatially transformed by the H matrix, and... |
Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper | Point2Skeleton: Learning Skeletal Representations from Point Clouds | [
"Cheng Lin",
"Changjian Li",
"Yuan Liu",
"Nenglun Chen",
"Yi-King Choi",
"Wenping Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Point2Skeleton_Learning_Skeletal_CVPR_2021_supplemental.pdf | 2012.00230 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Cheng and Li, Changjian and Liu, Yuan and Chen, Nenglun and Choi, Yi-King and Wang, Wenping},
title = {Point2Skeleton: Learning Skeletal Representations from Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and... |
Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper | Neighborhood Contrastive Learning for Novel Class Discovery | [
"Zhun Zhong",
"Enrico Fini",
"Subhankar Roy",
"Zhiming Luo",
"Elisa Ricci",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Neighborhood_Contrastive_Learning_CVPR_2021_supplemental.pdf | 2106.10731 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Zhun and Fini, Enrico and Roy, Subhankar and Luo, Zhiming and Ricci, Elisa and Sebe, Nicu},
title = {Neighborhood Contrastive Learning for Novel Class Discovery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are... |
Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper | SimPoE: Simulated Character Control for 3D Human Pose Estimation | [
"Ye Yuan",
"Shih-En Wei",
"Tomas Simon",
"Kris Kitani",
"Jason Saragih"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yuan_SimPoE_Simulated_Character_CVPR_2021_supplemental.zip | 2104.00683 | cvf | @InProceedings{Yuan_2021_CVPR,
author = {Yuan, Ye and Wei, Shih-En and Simon, Tomas and Kitani, Kris and Saragih, Jason},
title = {SimPoE: Simulated Character Control for 3D Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inferen... |
Ouyang_Neural_Camera_Simulators_CVPR_2021_paper | Neural Camera Simulators | [
"Hao Ouyang",
"Zifan Shi",
"Chenyang Lei",
"Ka Lung Law",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ouyang_Neural_Camera_Simulators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ouyang_Neural_Camera_Simulators_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ouyang_Neural_Camera_Simulators_CVPR_2021_supplemental.pdf | 2104.05237 | cvf | @InProceedings{Ouyang_2021_CVPR,
author = {Ouyang, Hao and Shi, Zifan and Lei, Chenyang and Law, Ka Lung and Chen, Qifeng},
title = {Neural Camera Simulators},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It ... |
Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper | Neighborhood Normalization for Robust Geometric Feature Learning | [
"Xingtong Liu",
"Benjamin D. Killeen",
"Ayushi Sinha",
"Masaru Ishii",
"Gregory D. Hager",
"Russell H. Taylor",
"Mathias Unberath"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Neighborhood_Normalization_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xingtong and Killeen, Benjamin D. and Sinha, Ayushi and Ishii, Masaru and Hager, Gregory D. and Taylor, Russell H. and Unberath, Mathias},
title = {Neighborhood Normalization for Robust Geometric Feature Learning},
booktitle = {Proceedings of the IEEE/CVF ... | Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation. Many hand-crafted and learning-based methods aim to produce consistent and distinguishable geometric features for 3D models with partial overlap. These methods work well in c... |
Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper | Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling | [
"Yan-Cheng Huang",
"Yi-Hsin Chen",
"Cheng-You Lu",
"Hui-Po Wang",
"Wen-Hsiao Peng",
"Ching-Chun Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Video_Rescaling_Networks_CVPR_2021_supplemental.pdf | 2103.14858 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Yan-Cheng and Chen, Yi-Hsin and Lu, Cheng-You and Wang, Hui-Po and Peng, Wen-Hsiao and Huang, Ching-Chun},
title = {Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling},
booktitle = {Proceedings of the IEEE/CVF Con... | This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize video downscaling and upscaling as a combined task. Most recent studies focus on image-based solutions, which do not consider temporal inform... |
Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper | TPCN: Temporal Point Cloud Networks for Motion Forecasting | [
"Maosheng Ye",
"Tongyi Cao",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper.pdf | null | 2103.03067 | cvf | @InProceedings{Ye_2021_CVPR,
author = {Ye, Maosheng and Cao, Tongyi and Chen, Qifeng},
title = {TPCN: Temporal Point Cloud Networks for Motion Forecasting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud lear... |
Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper | TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation | [
"Lingming Zhang",
"Yue Zhao",
"Deyu Meng",
"Zhiming Cui",
"Chenqiang Gao",
"Xinbo Gao",
"Chunfeng Lian",
"Dinggang Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.pdf | null | 2012.13697 | title_judge | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Lingming and Zhao, Yue and Meng, Deyu and Cui, Zhiming and Gao, Chenqiang and Gao, Xinbo and Lian, Chunfeng and Shen, Dinggang},
title = {TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Seg... | The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinate... |
Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper | Meta Batch-Instance Normalization for Generalizable Person Re-Identification | [
"Seokeon Choi",
"Taekyung Kim",
"Minki Jeong",
"Hyoungseob Park",
"Changick Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Meta_Batch-Instance_Normalization_CVPR_2021_supplemental.pdf | 2011.14670 | cvf | @InProceedings{Choi_2021_CVPR,
author = {Choi, Seokeon and Kim, Taekyung and Jeong, Minki and Park, Hyoungseob and Kim, Changick},
title = {Meta Batch-Instance Normalization for Generalizable Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce ... |
Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper | Dictionary-Guided Scene Text Recognition | [
"Nguyen Nguyen",
"Thu Nguyen",
"Vinh Tran",
"Minh-Triet Tran",
"Thanh Duc Ngo",
"Thien Huu Nguyen",
"Minh Hoai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_Dictionary-Guided_Scene_Text_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Nguyen_2021_CVPR,
author = {Nguyen, Nguyen and Nguyen, Thu and Tran, Vinh and Tran, Minh-Triet and Ngo, Thanh Duc and Nguyen, Thien Huu and Hoai, Minh},
title = {Dictionary-Guided Scene Text Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Language prior plays an important role in the way humans perceive and recognize text in the wild. In this work, we present an approach to train and use scene text recognition models by exploiting multiple clues from a language reference. Current scene text recognition methods have used lexicons to improve recognition p... |
Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper | Glance and Gaze: Inferring Action-Aware Points for One-Stage Human-Object Interaction Detection | [
"Xubin Zhong",
"Xian Qu",
"Changxing Ding",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Glance_and_Gaze_CVPR_2021_supplemental.pdf | 2104.05269 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Xubin and Qu, Xian and Ding, Changxing and Tao, Dacheng},
title = {Glance and Gaze: Inferring Action-Aware Points for One-Stage Human-Object Interaction Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Modern human-object interaction (HOI) detection approaches can be divided into one-stage methods and two-stage ones. One-stage models are more efficient due to their straightforward architectures, but the two-stage models are still advantageous in accuracy. Existing one-stage models usually begin by detecting predefine... |
Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper | Activate or Not: Learning Customized Activation | [
"Ningning Ma",
"Xiangyu Zhang",
"Ming Liu",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Activate_or_Not_CVPR_2021_supplemental.pdf | 2009.04759 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Ningning and Zhang, Xiangyu and Liu, Ming and Sun, Jian},
title = {Activate or Not: Learning Customized Activation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searched activation, can be interpreted as a smooth approximation to ReLU. Intuitively, in the same way, we approximate the more general Maxout f... |
Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper | Wide-Baseline Relative Camera Pose Estimation With Directional Learning | [
"Kefan Chen",
"Noah Snavely",
"Ameesh Makadia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Wide-Baseline_Relative_Camera_CVPR_2021_supplemental.pdf | 2106.03336 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Kefan and Snavely, Noah and Makadia, Ameesh},
title = {Wide-Baseline Relative Camera Pose Estimation With Directional Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images. These models continue to struggle even with t... |
Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper | Improving Unsupervised Image Clustering With Robust Learning | [
"Sungwon Park",
"Sungwon Han",
"Sundong Kim",
"Danu Kim",
"Sungkyu Park",
"Seunghoon Hong",
"Meeyoung Cha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Improving_Unsupervised_Image_CVPR_2021_supplemental.pdf | 2012.11150 | cvf | @InProceedings{Park_2021_CVPR,
author = {Park, Sungwon and Han, Sungwon and Kim, Sundong and Kim, Danu and Park, Sungkyu and Hong, Seunghoon and Cha, Meeyoung},
title = {Improving Unsupervised Image Clustering With Robust Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizi... |
Morreale_Neural_Surface_Maps_CVPR_2021_paper | Neural Surface Maps | [
"Luca Morreale",
"Noam Aigerman",
"Vladimir G. Kim",
"Niloy J. Mitra"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Morreale_Neural_Surface_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Morreale_Neural_Surface_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morreale_Neural_Surface_Maps_CVPR_2021_supplemental.pdf | 2103.16942 | cvf | @InProceedings{Morreale_2021_CVPR,
author = {Morreale, Luca and Aigerman, Noam and Kim, Vladimir G. and Mitra, Niloy J.},
title = {Neural Surface Maps},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubiquitous and are used in many core applications, such as paramterization, shape analysis, remeshing, and deformation. Unfortunately, most com... |
Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper | Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods | [
"Minghan Yang",
"Dong Xu",
"Hongyu Chen",
"Zaiwen Wen",
"Mengyun Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Enhance_Curvature_Information_CVPR_2021_supplemental.pdf | 2006.09606 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Minghan and Xu, Dong and Chen, Hongyu and Wen, Zaiwen and Chen, Mengyun},
title = {Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propo... |
Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper | Variational Relational Point Completion Network | [
"Liang Pan",
"Xinyi Chen",
"Zhongang Cai",
"Junzhe Zhang",
"Haiyu Zhao",
"Shuai Yi",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_Variational_Relational_Point_CVPR_2021_supplemental.pdf | 2104.10154 | cvf | @InProceedings{Pan_2021_CVPR,
author = {Pan, Liang and Chen, Xinyi and Cai, Zhongang and Zhang, Junzhe and Zhao, Haiyu and Yi, Shuai and Liu, Ziwei},
title = {Variational Relational Point Completion Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-m... |
Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper | StruMonoNet: Structure-Aware Monocular 3D Prediction | [
"Zhenpei Yang",
"Li Erran Li",
"Qixing Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_StruMonoNet_Structure-Aware_Monocular_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Zhenpei and Li, Li Erran and Huang, Qixing},
title = {StruMonoNet: Structure-Aware Monocular 3D Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {... | Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not fully utilize the underlying 3D environment's... |
Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | Learning To Relate Depth and Semantics for Unsupervised Domain Adaptation | [
"Suman Saha",
"Anton Obukhov",
"Danda Pani Paudel",
"Menelaos Kanakis",
"Yuhua Chen",
"Stamatios Georgoulis",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saha_Learning_To_Relate_CVPR_2021_supplemental.pdf | 2105.07830 | cvf | @InProceedings{Saha_2021_CVPR,
author = {Saha, Suman and Obukhov, Anton and Paudel, Danda Pani and Kanakis, Menelaos and Chen, Yuhua and Georgoulis, Stamatios and Van Gool, Luc},
title = {Learning To Relate Depth and Semantics for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/... | We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular depth estimation are shown to be complementary tasks; in a multi-task learning setting, a proper encoding of their relationships can further ... |
Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper | Training Networks in Null Space of Feature Covariance for Continual Learning | [
"Shipeng Wang",
"Xiaorong Li",
"Jian Sun",
"Zongben Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Training_Networks_in_CVPR_2021_supplemental.pdf | 2103.07113 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Shipeng and Li, Xiaorong and Sun, Jian and Xu, Zongben},
title = {Training Networks in Null Space of Feature Covariance for Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameter... |
Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper | PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering | [
"Jang Hyun Cho",
"Utkarsh Mall",
"Kavita Bala",
"Bharath Hariharan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cho_PiCIE_Unsupervised_Semantic_CVPR_2021_supplemental.pdf | 2103.17070 | cvf | @InProceedings{Cho_2021_CVPR,
author = {Cho, Jang Hyun and Mall, Utkarsh and Bala, Kavita and Hariharan, Bharath},
title = {PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign se... |
He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper | DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution | [
"Tong He",
"Chunhua Shen",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper.pdf | null | 2011.13328 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Tong and Shen, Chunhua and van den Hengel, Anton},
title = {DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Previous top-performing approaches for point cloud instance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions. The inevitable variatio... |
Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper | SSLayout360: Semi-Supervised Indoor Layout Estimation From 360deg Panorama | [
"Phi Vu Tran"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tran_SSLayout360_Semi-Supervised_Indoor_CVPR_2021_supplemental.pdf | 2103.13696 | title_judge | @InProceedings{Tran_2021_CVPR,
author = {Tran, Phi Vu},
title = {SSLayout360: Semi-Supervised Indoor Layout Estimation From 360deg Panorama},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pag... | Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to lear... |
Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper | SLADE: A Self-Training Framework for Distance Metric Learning | [
"Jiali Duan",
"Yen-Liang Lin",
"Son Tran",
"Larry S. Davis",
"C.-C. Jay Kuo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_SLADE_A_Self-Training_CVPR_2021_supplemental.pdf | 2011.10269 | cvf | @InProceedings{Duan_2021_CVPR,
author = {Duan, Jiali and Lin, Yen-Liang and Tran, Son and Davis, Larry S. and Kuo, C.-C. Jay},
title = {SLADE: A Self-Training Framework for Distance Metric Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generat... |
Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper | NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning | [
"Hyunho Ha",
"Joo Ho Lee",
"Andreas Meuleman",
"Min H. Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ha_NormalFusion_Real-Time_Acquisition_CVPR_2021_supplemental.zip | null | null | @InProceedings{Ha_2021_CVPR,
author = {Ha, Hyunho and Lee, Joo Ho and Meuleman, Andreas and Kim, Min H.},
title = {NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, ... |
Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper | SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud | [
"Wu Zheng",
"Weiliang Tang",
"Li Jiang",
"Chi-Wing Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_SE-SSD_Self-Ensembling_Single-Stage_CVPR_2021_supplemental.pdf | 2104.09804 | title_snapshot | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Wu and Tang, Weiliang and Jiang, Li and Fu, Chi-Wing},
title = {SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specif... |
Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper | Where and What? Examining Interpretable Disentangled Representations | [
"Xinqi Zhu",
"Chang Xu",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Where_and_What_CVPR_2021_supplemental.pdf | 2104.05622 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Xinqi and Xu, Chang and Tao, Dacheng},
title = {Where and What? Examining Interpretable Disentangled Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations ... |
Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper | Physically-Aware Generative Network for 3D Shape Modeling | [
"Mariem Mezghanni",
"Malika Boulkenafed",
"Andre Lieutier",
"Maks Ovsjanikov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mezghanni_Physically-Aware_Generative_Network_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Mezghanni_2021_CVPR,
author = {Mezghanni, Mariem and Boulkenafed, Malika and Lieutier, Andre and Ovsjanikov, Maks},
title = {Physically-Aware Generative Network for 3D Shape Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Shapes are often designed to satisfy structural properties and serve a particular functionality in the physical world. Unfortunately, most existing generative models focus primarily on the geometric or visual plausibility, ignoring the physical or structural constraints. To remedy this, we present a novel method aimed ... |
Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper | Bilinear Parameterization for Non-Separable Singular Value Penalties | [
"Marcus Valtonen Ornhag",
"Jose Pedro Iglesias",
"Carl Olsson"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ornhag_Bilinear_Parameterization_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Ornhag_2021_CVPR,
author = {Ornhag, Marcus Valtonen and Iglesias, Jose Pedro and Olsson, Carl},
title = {Bilinear Parameterization for Non-Separable Singular Value Penalties},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Low rank inducing penalties have been proven to successfully uncover fundamental structures considered in computer vision and machine learning; however, such methods generally lead to non-convex optimization problems. Since the resulting objective is non-convex one often resorts to using standard splitting schemes such... |
Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper | Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations | [
"Adel Ahmadyan",
"Liangkai Zhang",
"Artsiom Ablavatski",
"Jianing Wei",
"Matthias Grundmann"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmadyan_Objectron_A_Large_CVPR_2021_supplemental.pdf | 2012.09988 | cvf | @InProceedings{Ahmadyan_2021_CVPR,
author = {Ahmadyan, Adel and Zhang, Liangkai and Ablavatski, Artsiom and Wei, Jianing and Grundmann, Matthias},
title = {Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations},
booktitle = {Proceedings of the IEEE/CVF Conference... | 3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and impr... |
Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper | Intra-Inter Camera Similarity for Unsupervised Person Re-Identification | [
"Shiyu Xuan",
"Shiliang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.pdf | null | 2103.11658 | cvf | @InProceedings{Xuan_2021_CVPR,
author = {Xuan, Shiyu and Zhang, Shiliang},
title = {Intra-Inter Camera Similarity for Unsupervised Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year =... | Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-i... |
Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper | Efficient Feature Transformations for Discriminative and Generative Continual Learning | [
"Vinay Kumar Verma",
"Kevin J Liang",
"Nikhil Mehta",
"Piyush Rai",
"Lawrence Carin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Verma_Efficient_Feature_Transformations_CVPR_2021_supplemental.zip | 2103.13558 | cvf | @InProceedings{Verma_2021_CVPR,
author = {Verma, Vinay Kumar and Liang, Kevin J and Mehta, Nikhil and Rai, Piyush and Carin, Lawrence},
title = {Efficient Feature Transformations for Discriminative and Generative Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critical. Methods incorporating network expansion have shown promise by naturally adding model capacity for learning new tasks while simultaneously... |
Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper | Learning a Self-Expressive Network for Subspace Clustering | [
"Shangzhi Zhang",
"Chong You",
"Rene Vidal",
"Chun-Guang Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper.pdf | null | 2110.04318 | title_snapshot | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Shangzhi and You, Chong and Vidal, Rene and Li, Chun-Guang},
title = {Learning a Self-Expressive Network for Subspace Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | State-of-the-art subspace clustering methods are based on the self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-... |
Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper | A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning | [
"Christoph Feichtenhofer",
"Haoqi Fan",
"Bo Xiong",
"Ross Girshick",
"Kaiming He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feichtenhofer_A_Large-Scale_Study_CVPR_2021_supplemental.pdf | 2104.14558 | cvf | @InProceedings{Feichtenhofer_2021_CVPR,
author = {Feichtenhofer, Christoph and Fan, Haoqi and Xiong, Bo and Girshick, Ross and He, Kaiming},
title = {A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same... |
Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper | Asymmetric Metric Learning for Knowledge Transfer | [
"Mateusz Budnik",
"Yannis Avrithis"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Budnik_Asymmetric_Metric_Learning_CVPR_2021_supplemental.pdf | 2006.16331 | cvf | @InProceedings{Budnik_2021_CVPR,
author = {Budnik, Mateusz and Avrithis, Yannis},
title = {Asymmetric Metric Learning for Knowledge Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pa... | Knowledge transfer from large teacher models to smaller student models has recently been studied for metric learning, focusing on fine-grained classification. In this work, focusing on instance-level image retrieval, we study an asymmetric testing task, where the database is represented by the teacher and queries by th... |
Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper | Frequency-Aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection | [
"Jiaming Li",
"Hongtao Xie",
"Jiahong Li",
"Zhongyuan Wang",
"Yongdong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Frequency-Aware_Discriminative_Feature_CVPR_2021_supplemental.pdf | 2103.09096 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Jiaming and Xie, Hongtao and Li, Jiahong and Wang, Zhongyuan and Zhang, Yongdong},
title = {Frequency-Aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on C... | Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features supervised by softmax loss are separable but not discriminative enoug... |
Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper | 3DCaricShop: A Dataset and a Baseline Method for Single-View 3D Caricature Face Reconstruction | [
"Yuda Qiu",
"Xiaojie Xu",
"Lingteng Qiu",
"Yan Pan",
"Yushuang Wu",
"Weikai Chen",
"Xiaoguang Han"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_3DCaricShop_A_Dataset_CVPR_2021_supplemental.pdf | 2103.08204 | cvf | @InProceedings{Qiu_2021_CVPR,
author = {Qiu, Yuda and Xu, Xiaojie and Qiu, Lingteng and Pan, Yan and Wu, Yushuang and Chen, Weikai and Han, Xiaoguang},
title = {3DCaricShop: A Dataset and a Baseline Method for Single-View 3D Caricature Face Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Co... | Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricSho... |
Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper | OCONet: Image Extrapolation by Object Completion | [
"Richard Strong Bowen",
"Huiwen Chang",
"Charles Herrmann",
"Piotr Teterwak",
"Ce Liu",
"Ramin Zabih"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bowen_OCONet_Image_Extrapolation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Bowen_2021_CVPR,
author = {Bowen, Richard Strong and Chang, Huiwen and Herrmann, Charles and Teterwak, Piotr and Liu, Ce and Zabih, Ramin},
title = {OCONet: Image Extrapolation by Object Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Image extrapolation extends an input image beyond the originally-captured field of view. Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes. We introduce OCONet (Object COmpletion N... |
Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper | VisualVoice: Audio-Visual Speech Separation With Cross-Modal Consistency | [
"Ruohan Gao",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper.pdf | null | 2101.03149 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Ruohan and Grauman, Kristen},
title = {VisualVoice: Audio-Visual Speech Separation With Cross-Modal Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker's lip movements and the sounds they... |
Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper | Fair Attribute Classification Through Latent Space De-Biasing | [
"Vikram V. Ramaswamy",
"Sunnie S. Y. Kim",
"Olga Russakovsky"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ramaswamy_Fair_Attribute_Classification_CVPR_2021_supplemental.pdf | 2012.01469 | cvf | @InProceedings{Ramaswamy_2021_CVPR,
author = {Ramaswamy, Vikram V. and Kim, Sunnie S. Y. and Russakovsky, Olga},
title = {Fair Attribute Classification Through Latent Space De-Biasing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in which target labels are correlated with protected attributes (e.g., gender, race) are known to learn and exploit those correlations. In this... |
Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper | Correlated Input-Dependent Label Noise in Large-Scale Image Classification | [
"Mark Collier",
"Basil Mustafa",
"Efi Kokiopoulou",
"Rodolphe Jenatton",
"Jesse Berent"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Collier_Correlated_Input-Dependent_Label_CVPR_2021_supplemental.zip | 2105.10305 | cvf | @InProceedings{Collier_2021_CVPR,
author = {Collier, Mark and Mustafa, Basil and Kokiopoulou, Efi and Jenatton, Rodolphe and Berent, Jesse},
title = {Correlated Input-Dependent Label Noise in Large-Scale Image Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a multivariate Normal distributed latent variable on the final hidden layer of a neural network classifie... |
Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper | Delving Into Localization Errors for Monocular 3D Object Detection | [
"Xinzhu Ma",
"Yinmin Zhang",
"Dan Xu",
"Dongzhan Zhou",
"Shuai Yi",
"Haojie Li",
"Wanli Ouyang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Delving_Into_Localization_CVPR_2021_supplemental.pdf | 2103.16237 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Xinzhu and Zhang, Yinmin and Xu, Dan and Zhou, Dongzhan and Yi, Shuai and Li, Haojie and Ouyang, Wanli},
title = {Delving Into Localization Errors for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the `localization error' is th... |
Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper | Nearest Neighbor Matching for Deep Clustering | [
"Zhiyuan Dang",
"Cheng Deng",
"Xu Yang",
"Kun Wei",
"Heng Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Dang_2021_CVPR,
author = {Dang, Zhiyuan and Deng, Cheng and Yang, Xu and Wei, Kun and Huang, Heng},
title = {Nearest Neighbor Matching for Deep Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relati... |
Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper | MOOD: Multi-Level Out-of-Distribution Detection | [
"Ziqian Lin",
"Sreya Dutta Roy",
"Yixuan Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_MOOD_Multi-Level_Out-of-Distribution_CVPR_2021_supplemental.pdf | 2104.14726 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Ziqian and Roy, Sreya Dutta and Li, Yixuan},
title = {MOOD: Multi-Level Out-of-Distribution Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full feedforward pass for any given input. In this paper, we propose a novel framework,... |
Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper | Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection | [
"Jingru Tan",
"Xin Lu",
"Gang Zhang",
"Changqing Yin",
"Quanquan Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Equalization_Loss_v2_CVPR_2021_supplemental.pdf | 2012.08548 | cvf | @InProceedings{Tan_2021_CVPR,
author = {Tan, Jingru and Lu, Xin and Zhang, Gang and Yin, Changqing and Li, Quanquan},
title = {Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimization of representation and classifier might lead to suboptimal results. However, end-to-end training methods, like equalization loss (EQL),... |
Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper | Dynamic Metric Learning: Towards a Scalable Metric Space To Accommodate Multiple Semantic Scales | [
"Yifan Sun",
"Yuke Zhu",
"Yuhan Zhang",
"Pengkun Zheng",
"Xi Qiu",
"Chi Zhang",
"Yichen Wei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper.pdf | null | 2103.11781 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Yifan and Zhu, Yuke and Zhang, Yuhan and Zheng, Pengkun and Qiu, Xi and Zhang, Chi and Wei, Yichen},
title = {Dynamic Metric Learning: Towards a Scalable Metric Space To Accommodate Multiple Semantic Scales},
booktitle = {Proceedings of the IEEE/CVF Confer... | This paper introduces a new fundamental characteristics, i.e., the dynamic range, from real-world metric tools to deep visual recognition. In metrology, the dynamic range is a basic quality of a metric tool, indicating its flexibility to accommodate various scales. Larger dynamic range offers higher flexibility. We arg... |
Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper | Primitive Representation Learning for Scene Text Recognition | [
"Ruijie Yan",
"Liangrui Peng",
"Shanyu Xiao",
"Gang Yao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Primitive_Representation_Learning_CVPR_2021_supplemental.pdf | 2105.04286 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Ruijie and Peng, Liangrui and Xiao, Shanyu and Yao, Gang},
title = {Primitive Representation Learning for Scene Text Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Scene text recognition is a challenging task due to diverse variations of text instances in natural scene images. Conventional methods based on CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully investigate stable and efficient feature representations for multi-oriented scene texts. In this paper, we... |
Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper | RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation | [
"Sk Aziz Ali",
"Kerem Kahraman",
"Gerd Reis",
"Didier Stricker"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ali_RPSRNet_End-to-End_Trainable_CVPR_2021_supplemental.pdf | 2104.05328 | title_judge | @InProceedings{Ali_2021_CVPR,
author = {Ali, Sk Aziz and Kahraman, Kerem and Reis, Gerd and Stricker, Didier},
title = {RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | We propose RPSRNet - a novel end-to-end trainable deep neural network for rigid point set registration. For this task, we use a novel 2^D-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network. An iterative transformation refinement module in our network boosts the ... |
Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper | On the Difficulty of Membership Inference Attacks | [
"Shahbaz Rezaei",
"Xin Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rezaei_On_the_Difficulty_CVPR_2021_supplemental.pdf | 2005.13702 | cvf | @InProceedings{Rezaei_2021_CVPR,
author = {Rezaei, Shahbaz and Liu, Xin},
title = {On the Difficulty of Membership Inference Attacks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process. Despite their apparent success, these studies only report accuracy, precision, and recall of the positive class (member class). Hence, the performance of these attacks h... |
Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper | Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D Shapes | [
"Towaki Takikawa",
"Joey Litalien",
"Kangxue Yin",
"Karsten Kreis",
"Charles Loop",
"Derek Nowrouzezahrai",
"Alec Jacobson",
"Morgan McGuire",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Takikawa_Neural_Geometric_Level_CVPR_2021_supplemental.pdf | 2101.10994 | cvf | @InProceedings{Takikawa_2021_CVPR,
author = {Takikawa, Towaki and Litalien, Joey and Yin, Kangxue and Kreis, Karsten and Loop, Charles and Nowrouzezahrai, Derek and Jacobson, Alec and McGuire, Morgan and Fidler, Sanja},
title = {Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D Shape... | Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive si... |
Song_Pareidolia_Face_Reenactment_CVPR_2021_paper | Pareidolia Face Reenactment | [
"Linsen Song",
"Wayne Wu",
"Chaoyou Fu",
"Chen Qian",
"Chen Change Loy",
"Ran He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Pareidolia_Face_Reenactment_CVPR_2021_supplemental.zip | 2104.03061 | cvf | @InProceedings{Song_2021_CVPR,
author = {Song, Linsen and Wu, Wayne and Fu, Chaoyou and Qian, Chen and Loy, Chen Change and He, Ran},
title = {Pareidolia Face Reenactment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i.e.,... |
Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper | ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks | [
"Xinshao Wang",
"Yang Hua",
"Elyor Kodirov",
"David A. Clifton",
"Neil M. Robertson"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_ProSelfLC_Progressive_Self_CVPR_2021_supplemental.pdf | 2005.03788 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Clifton, David A. and Robertson, Neil M.},
title = {ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealing as it exploits its own knowledge and requires no extra models. Howev... |
Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper | Learning To Segment Rigid Motions From Two Frames | [
"Gengshan Yang",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Learning_To_Segment_CVPR_2021_supplemental.pdf | 2101.03694 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Gengshan and Ramanan, Deva},
title = {Learning To Segment Rigid Motions From Two Frames},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performan... |
Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper | Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI | [
"Yohan Jun",
"Hyungseob Shin",
"Taejoon Eo",
"Dosik Hwang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Jun_2021_CVPR,
author = {Jun, Yohan and Shin, Hyungseob and Eo, Taejoon and Hwang, Dosik},
title = {Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution and high-contrast images. However, MRI requires a relatively long scan time compared to other medical imaging techniques, where long scan time might occur patient's discomfort and limit the increase in resolution of magnetic resona... |
Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper | On Feature Normalization and Data Augmentation | [
"Boyi Li",
"Felix Wu",
"Ser-Nam Lim",
"Serge Belongie",
"Kilian Q. Weinberger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_On_Feature_Normalization_CVPR_2021_supplemental.pdf | 2002.11102 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Boyi and Wu, Felix and Lim, Ser-Nam and Belongie, Serge and Weinberger, Kilian Q.},
title = {On Feature Normalization and Data Augmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from... |
Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper | SelfDoc: Self-Supervised Document Representation Learning | [
"Peizhao Li",
"Jiuxiang Gu",
"Jason Kuen",
"Vlad I. Morariu",
"Handong Zhao",
"Rajiv Jain",
"Varun Manjunatha",
"Hongfu Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_SelfDoc_Self-Supervised_Document_CVPR_2021_supplemental.pdf | 2106.03331 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Peizhao and Gu, Jiuxiang and Kuen, Jason and Morariu, Vlad I. and Zhao, Handong and Jain, Rajiv and Manjunatha, Varun and Liu, Hongfu},
title = {SelfDoc: Self-Supervised Document Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference o... | We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework exploits the positional, textual, and visual information of every semantically meaningful component in a document, and it models the contex... |
Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper | Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes | [
"Zhihang Zhong",
"Yinqiang Zheng",
"Imari Sato"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Towards_Rolling_Shutter_CVPR_2021_supplemental.zip | 2104.01601 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Zhihang and Zheng, Yinqiang and Sato, Imari},
title = {Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Joint rolling shutter correction and deblurring (RSCD) techniques are critical for the prevalent CMOS cameras. However, current approaches are still based on conventional energy optimization and are developed for static scenes. To enable learning-based approaches to address real-world RSCD problem, we contribute the fi... |
Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper | VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild | [
"Jiaxu Miao",
"Yunchao Wei",
"Yu Wu",
"Chen Liang",
"Guangrui Li",
"Yi Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Miao_VSPW_A_Large-scale_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Miao_2021_CVPR,
author = {Miao, Jiaxu and Wei, Yunchao and Wu, Yu and Liang, Chen and Li, Guangrui and Yang, Yi},
title = {VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | In this paper, we present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1) Wel... |
Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper | Multi-Label Learning From Single Positive Labels | [
"Elijah Cole",
"Oisin Mac Aodha",
"Titouan Lorieul",
"Pietro Perona",
"Dan Morris",
"Nebojsa Jojic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper.pdf | null | 2106.09708 | cvf | @InProceedings{Cole_2021_CVPR,
author = {Cole, Elijah and Mac Aodha, Oisin and Lorieul, Titouan and Perona, Pietro and Morris, Dan and Jojic, Nebojsa},
title = {Multi-Label Learning From Single Positive Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human an... |
Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper | Towards Part-Based Understanding of RGB-D Scans | [
"Alexey Bokhovkin",
"Vladislav Ishimtsev",
"Emil Bogomolov",
"Denis Zorin",
"Alexey Artemov",
"Evgeny Burnaev",
"Angela Dai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bokhovkin_Towards_Part-Based_Understanding_CVPR_2021_supplemental.pdf | 2012.02094 | title_snapshot | @InProceedings{Bokhovkin_2021_CVPR,
author = {Bokhovkin, Alexey and Ishimtsev, Vladislav and Bogomolov, Emil and Zorin, Denis and Artemov, Alexey and Burnaev, Evgeny and Dai, Angela},
title = {Towards Part-Based Understanding of RGB-D Scans},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-bas... |
Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper | Learning Semantic-Aware Dynamics for Video Prediction | [
"Xinzhu Bei",
"Yanchao Yang",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper.pdf | null | 2104.09762 | cvf | @InProceedings{Bei_2021_CVPR,
author = {Bei, Xinzhu and Yang, Yanchao and Soatto, Stefano},
title = {Learning Semantic-Aware Dynamics for Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context... |
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