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0 | AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition | [
"Lei Shi",
"Yifan Zhang",
"Jian Cheng",
"Hanqing Lu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Shi_AdaSGN_Adapting_Joint_Number_and_Model_Size_for_Efficient_Skeleton-Based_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Shi_AdaSGN_Adapting_Joint_Number_and_Model_Size_for_Efficient_Skeleton-Based_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Shi_AdaSGN_Adapting_Joint_ICCV_2021_supplemental.pdf | 2103.11770 | cvf | @InProceedings{Shi_2021_ICCV,
author = {Shi, Lei and Zhang, Yifan and Cheng, Jian and Lu, Hanqing},
title = {AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there are some works trying to speed up the skeleton modeling by designing light-weight modules. However, in addition to the model size, the amou... | [
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1 | C2N: Practical Generative Noise Modeling for Real-World Denoising | [
"Geonwoon Jang",
"Wooseok Lee",
"Sanghyun Son",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jang_C2N_Practical_Generative_Noise_Modeling_for_Real-World_Denoising_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jang_C2N_Practical_Generative_Noise_Modeling_for_Real-World_Denoising_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Jang_C2N_Practical_Generative_ICCV_2021_supplemental.pdf | 2202.09533 | title_snapshot | @InProceedings{Jang_2021_ICCV,
author = {Jang, Geonwoon and Lee, Wooseok and Son, Sanghyun and Lee, Kyoung Mu},
title = {C2N: Practical Generative Noise Modeling for Real-World Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | Learning-based image denoising methods have been bounded to situations where well-aligned noisy and clean images are given, or samples are synthesized from predetermined noise models, e.g., Gaussian. While recent generative noise modeling methods aim to simulate the unknown distribution of real-world noise, several lim... | [
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2 | Continual Learning on Noisy Data Streams via Self-Purified Replay | [
"Chris Dongjoo Kim",
"Jinseo Jeong",
"Sangwoo Moon",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Continual_Learning_on_Noisy_Data_Streams_via_Self-Purified_Replay_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Continual_Learning_on_Noisy_Data_Streams_via_Self-Purified_Replay_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_Continual_Learning_on_ICCV_2021_supplemental.pdf | 2110.07735 | cvf | @InProceedings{Kim_2021_ICCV,
author = {Kim, Chris Dongjoo and Jeong, Jinseo and Moon, Sangwoo and Kim, Gunhee},
title = {Continual Learning on Noisy Data Streams via Self-Purified Replay},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {... | Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a replay-based continual learning framework that simultaneously addresses both catastrophic forgetting and noisy labels for the first time. Our solution is based on ... | [
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3 | FOVEA: Foveated Image Magnification for Autonomous Navigation | [
"Chittesh Thavamani",
"Mengtian Li",
"Nicolas Cebron",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Thavamani_FOVEA_Foveated_Image_Magnification_for_Autonomous_Navigation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Thavamani_FOVEA_Foveated_Image_Magnification_for_Autonomous_Navigation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Thavamani_FOVEA_Foveated_Image_ICCV_2021_supplemental.pdf | 2108.12102 | cvf | @InProceedings{Thavamani_2021_ICCV,
author = {Thavamani, Chittesh and Li, Mengtian and Cebron, Nicolas and Ramanan, Deva},
title = {FOVEA: Foveated Image Magnification for Autonomous Navigation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Efficient processing of high-resolution video streams is safety-critical for many robotics applications such as autonomous driving. Image downsampling is a commonly adopted technique to ensure the latency constraint is met. However, this naive approach greatly restricts an object detector's capability to identify small... | [
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4 | PlenOctrees for Real-Time Rendering of Neural Radiance Fields | [
"Alex Yu",
"Ruilong Li",
"Matthew Tancik",
"Hao Li",
"Ren Ng",
"Angjoo Kanazawa"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yu_PlenOctrees_for_Real-Time_Rendering_of_Neural_Radiance_Fields_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yu_PlenOctrees_for_Real-Time_Rendering_of_Neural_Radiance_Fields_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yu_PlenOctrees_for_Real-Time_ICCV_2021_supplemental.pdf | 2103.14024 | cvf | @InProceedings{Yu_2021_ICCV,
author = {Yu, Alex and Li, Ruilong and Tancik, Matthew and Li, Hao and Ng, Ren and Kanazawa, Angjoo},
title = {PlenOctrees for Real-Time Rendering of Neural Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method can render 800x800 images at more than 150 FPS, which is over 3000 times faster than conventional NeRFs. We do so without sacrificing quality... | [
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5 | Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation | [
"Robin Chan",
"Matthias Rottmann",
"Hanno Gottschalk"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chan_Entropy_Maximization_and_Meta_Classification_for_Out-of-Distribution_Detection_in_Semantic_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chan_Entropy_Maximization_and_Meta_Classification_for_Out-of-Distribution_Detection_in_Semantic_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chan_Entropy_Maximization_and_ICCV_2021_supplemental.pdf | 2012.06575 | cvf | @InProceedings{Chan_2021_ICCV,
author = {Chan, Robin and Rottmann, Matthias and Gottschalk, Hanno},
title = {Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (... | Deep neural networks (DNNs) for the semantic segmentation of images are usually trained to operate on a predefined closed set of object classes. This is in contrast to the ""open world"" setting where DNNs are envisioned to be deployed to. From a functional safety point of view, the ability to detect so-called ""out-of... | [
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6 | Specificity-Preserving RGB-D Saliency Detection | [
"Tao Zhou",
"Huazhu Fu",
"Geng Chen",
"Yi Zhou",
"Deng-Ping Fan",
"Ling Shao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhou_Specificity-Preserving_RGB-D_Saliency_ICCV_2021_supplemental.pdf | 2108.08162 | title_snapshot | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Tao and Fu, Huazhu and Chen, Geng and Zhou, Yi and Fan, Deng-Ping and Shao, Ling},
title = {Specificity-Preserving RGB-D Saliency Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing works often focus on learning a shared representation through various fusion strategies, with few methods explicitly considering how to preserve modality-specific... | [
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7 | 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds | [
"Lichen Zhao",
"Daigang Cai",
"Lu Sheng",
"Dong Xu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Lichen and Cai, Daigang and Sheng, Lu and Xu, Dong},
title = {3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octobe... | Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features, but... | [
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8 | 4D-Net for Learned Multi-Modal Alignment | [
"AJ Piergiovanni",
"Vincent Casser",
"Michael S. Ryoo",
"Anelia Angelova"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Piergiovanni_4D-Net_for_Learned_Multi-Modal_Alignment_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Piergiovanni_4D-Net_for_Learned_Multi-Modal_Alignment_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Piergiovanni_4D-Net_for_Learned_ICCV_2021_supplemental.pdf | 2109.01066 | cvf | @InProceedings{Piergiovanni_2021_ICCV,
author = {Piergiovanni, AJ and Casser, Vincent and Ryoo, Michael S. and Angelova, Anelia},
title = {4D-Net for Learned Multi-Modal Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October}... | We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time. We are able to incorporate the 4D information by performing a novel dynamic connection learning across various feature representations and levels of abstraction and by observing geometric constrai... | [
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9 | Patch Craft: Video Denoising by Deep Modeling and Patch Matching | [
"Gregory Vaksman",
"Michael Elad",
"Peyman Milanfar"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Vaksman_Patch_Craft_Video_Denoising_by_Deep_Modeling_and_Patch_Matching_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Vaksman_Patch_Craft_Video_ICCV_2021_supplemental.pdf | 2103.13767 | cvf | @InProceedings{Vaksman_2021_ICCV,
author = {Vaksman, Gregory and Elad, Michael and Milanfar, Peyman},
title = {Patch Craft: Video Denoising by Deep Modeling and Patch Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented al... | [
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10 | Image Manipulation Detection by Multi-View Multi-Scale Supervision | [
"Xinru Chen",
"Chengbo Dong",
"Jiaqi Ji",
"Juan Cao",
"Xirong Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Image_Manipulation_Detection_by_Multi-View_Multi-Scale_Supervision_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Image_Manipulation_Detection_by_Multi-View_Multi-Scale_Supervision_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Image_Manipulation_Detection_ICCV_2021_supplemental.pdf | 2104.06832 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Xinru and Dong, Chengbo and Ji, Jiaqi and Cao, Juan and Li, Xirong},
title = {Image Manipulation Detection by Multi-View Multi-Scale Supervision},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by ... | [
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11 | Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation | [
"Yachao Zhang",
"Yanyun Qu",
"Yuan Xie",
"Zonghao Li",
"Shanshan Zheng",
"Cuihua Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Perturbed_Self-Distillation_Weakly_Supervised_Large-Scale_Point_Cloud_Semantic_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Perturbed_Self-Distillation_Weakly_Supervised_Large-Scale_Point_Cloud_Semantic_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhang_Perturbed_Self-Distillation_Weakly_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Yachao and Qu, Yanyun and Xie, Yuan and Li, Zonghao and Zheng, Shanshan and Li, Cuihua},
title = {Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conf... | Large-scale point cloud semantic segmentation has wide applications. Current popular researches mainly focus on fully supervised learning which demands expensive and tedious manual point-wise annotation. Weakly supervised learning is an alternative way to avoid this exhausting annotation. However, for large-scale point... | [
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12 | Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation | [
"Mikhail Usvyatsov",
"Anastasia Makarova",
"Rafael Ballester-Ripoll",
"Maxim Rakhuba",
"Andreas Krause",
"Konrad Schindler"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Usvyatsov_Cherry-Picking_Gradients_Learning_Low-Rank_Embeddings_of_Visual_Data_via_Differentiable_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Usvyatsov_Cherry-Picking_Gradients_Learning_Low-Rank_Embeddings_of_Visual_Data_via_Differentiable_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Usvyatsov_Cherry-Picking_Gradients_Learning_ICCV_2021_supplemental.pdf | 2105.14250 | title_snapshot | @InProceedings{Usvyatsov_2021_ICCV,
author = {Usvyatsov, Mikhail and Makarova, Anastasia and Ballester-Ripoll, Rafael and Rakhuba, Maxim and Krause, Andreas and Schindler, Konrad},
title = {Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation},
... | We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network encoder with a tensor train decomposition to learn a low-rank latent encoding, coupled with cross-approximation (CA) to learn the representatio... | [
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13 | Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query | [
"Guanyu Cai",
"Jun Zhang",
"Xinyang Jiang",
"Yifei Gong",
"Lianghua He",
"Fufu Yu",
"Pai Peng",
"Xiaowei Guo",
"Feiyue Huang",
"Xing Sun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cai_AskConfirm_Active_Detail_Enriching_for_Cross-Modal_Retrieval_With_Partial_Query_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cai_AskConfirm_Active_Detail_Enriching_for_Cross-Modal_Retrieval_With_Partial_Query_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Cai_AskConfirm_Active_Detail_ICCV_2021_supplemental.zip | 2103.01654 | title_snapshot | @InProceedings{Cai_2021_ICCV,
author = {Cai, Guanyu and Zhang, Jun and Jiang, Xinyang and Gong, Yifei and He, Lianghua and Yu, Fufu and Peng, Pai and Guo, Xiaowei and Huang, Feiyue and Sun, Xing},
title = {Ask\&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query},
booktitle ... | Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this wo... | [
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14 | EventHands: Real-Time Neural 3D Hand Pose Estimation From an Event Stream | [
"Viktor Rudnev",
"Vladislav Golyanik",
"Jiayi Wang",
"Hans-Peter Seidel",
"Franziska Mueller",
"Mohamed Elgharib",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Rudnev_EventHands_Real-Time_Neural_3D_Hand_Pose_Estimation_From_an_Event_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Rudnev_EventHands_Real-Time_Neural_3D_Hand_Pose_Estimation_From_an_Event_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Rudnev_EventHands_Real-Time_Neural_ICCV_2021_supplemental.pdf | 2012.06475 | cvf | @InProceedings{Rudnev_2021_ICCV,
author = {Rudnev, Viktor and Golyanik, Vladislav and Wang, Jiayi and Seidel, Hans-Peter and Mueller, Franziska and Elgharib, Mohamed and Theobalt, Christian},
title = {EventHands: Real-Time Neural 3D Hand Pose Estimation From an Event Stream},
booktitle = {Proceedings... | 3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor reacting on brightness changes. Our EventHands approach has characteristics previ... | [
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15 | Composable Augmentation Encoding for Video Representation Learning | [
"Chen Sun",
"Arsha Nagrani",
"Yonglong Tian",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Sun_Composable_Augmentation_Encoding_for_Video_Representation_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Sun_Composable_Augmentation_Encoding_for_Video_Representation_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Sun_Composable_Augmentation_Encoding_ICCV_2021_supplemental.pdf | 2104.00616 | cvf | @InProceedings{Sun_2021_ICCV,
author = {Sun, Chen and Nagrani, Arsha and Tian, Yonglong and Schmid, Cordelia},
title = {Composable Augmentation Encoding for Video Representation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. These methods implicitly assume a set of representational invari... | [
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16 | Exploiting Explanations for Model Inversion Attacks | [
"Xuejun Zhao",
"Wencan Zhang",
"Xiaokui Xiao",
"Brian Lim"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Exploiting_Explanations_for_Model_Inversion_Attacks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Exploiting_Explanations_for_Model_Inversion_Attacks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhao_Exploiting_Explanations_for_ICCV_2021_supplemental.pdf | 2104.12669 | cvf | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Xuejun and Zhang, Wencan and Xiao, Xiaokui and Lim, Brian},
title = {Exploiting Explanations for Model Inversion Attacks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy. Explainable artificial intelligence (XAI) provides more information to help users to understand model decisions, yet this additional knowled... | [
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17 | Semantic Diversity Learning for Zero-Shot Multi-Label Classification | [
"Avi Ben-Cohen",
"Nadav Zamir",
"Emanuel Ben-Baruch",
"Itamar Friedman",
"Lihi Zelnik-Manor"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ben-Cohen_Semantic_Diversity_Learning_for_Zero-Shot_Multi-Label_Classification_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ben-Cohen_Semantic_Diversity_Learning_for_Zero-Shot_Multi-Label_Classification_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ben-Cohen_Semantic_Diversity_Learning_ICCV_2021_supplemental.pdf | 2105.05926 | title_snapshot | @InProceedings{Ben-Cohen_2021_ICCV,
author = {Ben-Cohen, Avi and Zamir, Nadav and Ben-Baruch, Emanuel and Friedman, Itamar and Zelnik-Manor, Lihi},
title = {Semantic Diversity Learning for Zero-Shot Multi-Label Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comp... | Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world ca... | [
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18 | Describing and Localizing Multiple Changes With Transformers | [
"Yue Qiu",
"Shintaro Yamamoto",
"Kodai Nakashima",
"Ryota Suzuki",
"Kenji Iwata",
"Hirokatsu Kataoka",
"Yutaka Satoh"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Qiu_Describing_and_Localizing_Multiple_Changes_With_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Qiu_Describing_and_Localizing_Multiple_Changes_With_Transformers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Qiu_Describing_and_Localizing_ICCV_2021_supplemental.pdf | 2103.14146 | cvf | @InProceedings{Qiu_2021_ICCV,
author = {Qiu, Yue and Yamamoto, Shintaro and Nakashima, Kodai and Suzuki, Ryota and Iwata, Kenji and Kataoka, Hirokatsu and Satoh, Yutaka},
title = {Describing and Localizing Multiple Changes With Transformers},
booktitle = {Proceedings of the IEEE/CVF International Con... | Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-change captioning dataset... | [
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19 | Score-Based Point Cloud Denoising | [
"Shitong Luo",
"Wei Hu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Luo_Score-Based_Point_Cloud_Denoising_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Luo_Score-Based_Point_Cloud_Denoising_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Luo_Score-Based_Point_Cloud_ICCV_2021_supplemental.pdf | 2107.10981 | cvf | @InProceedings{Luo_2021_ICCV,
author = {Luo, Shitong and Hu, Wei},
title = {Score-Based Point Cloud Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {4583-4592}
} | Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) who... | [
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20 | Panoptic Segmentation of Satellite Image Time Series With Convolutional Temporal Attention Networks | [
"Vivien Sainte Fare Garnot",
"Loic Landrieu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Garnot_Panoptic_Segmentation_of_Satellite_Image_Time_Series_With_Convolutional_Temporal_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Garnot_Panoptic_Segmentation_of_Satellite_Image_Time_Series_With_Convolutional_Temporal_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Garnot_Panoptic_Segmentation_of_ICCV_2021_supplemental.pdf | 2107.07933 | cvf | @InProceedings{Garnot_2021_ICCV,
author = {Garnot, Vivien Sainte Fare and Landrieu, Loic},
title = {Panoptic Segmentation of Satellite Image Time Series With Convolutional Temporal Attention Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
m... | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we arg... | [
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21 | Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring | [
"Omiros Pantazis",
"Gabriel J. Brostow",
"Kate E. Jones",
"Oisin Mac Aodha"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Pantazis_Focus_on_the_Positives_Self-Supervised_Learning_for_Biodiversity_Monitoring_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Pantazis_Focus_on_the_Positives_Self-Supervised_Learning_for_Biodiversity_Monitoring_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Pantazis_Focus_on_the_ICCV_2021_supplemental.pdf | 2108.06435 | cvf | @InProceedings{Pantazis_2021_ICCV,
author = {Pantazis, Omiros and Brostow, Gabriel J. and Jones, Kate E. and Mac Aodha, Oisin},
title = {Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision ... | We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or by speculatively picking negative samples, we instead also make use of the natu... | [
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22 | Bridging Unsupervised and Supervised Depth From Focus via All-in-Focus Supervision | [
"Ning-Hsu Wang",
"Ren Wang",
"Yu-Lun Liu",
"Yu-Hao Huang",
"Yu-Lin Chang",
"Chia-Ping Chen",
"Kevin Jou"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Bridging_Unsupervised_and_Supervised_Depth_From_Focus_via_All-in-Focus_Supervision_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Bridging_Unsupervised_and_Supervised_Depth_From_Focus_via_All-in-Focus_Supervision_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Bridging_Unsupervised_and_ICCV_2021_supplemental.pdf | 2108.10843 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Ning-Hsu and Wang, Ren and Liu, Yu-Lun and Huang, Yu-Hao and Chang, Yu-Lin and Chen, Chia-Ping and Jou, Kevin},
title = {Bridging Unsupervised and Supervised Depth From Focus via All-in-Focus Supervision},
booktitle = {Proceedings of the IEEE/CVF Interna... | Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another... | [
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23 | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions | [
"Wenhai Wang",
"Enze Xie",
"Xiang Li",
"Deng-Ping Fan",
"Kaitao Song",
"Ding Liang",
"Tong Lu",
"Ping Luo",
"Ling Shao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Pyramid_Vision_Transformer_A_Versatile_Backbone_for_Dense_Prediction_Without_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Pyramid_Vision_Transformer_A_Versatile_Backbone_for_Dense_Prediction_Without_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Pyramid_Vision_Transformer_ICCV_2021_supplemental.pdf | 2102.12122 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
title = {Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions},
booktitle = {Proceedi... | Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network useful for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we intr... | [
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24 | DOLG: Single-Stage Image Retrieval With Deep Orthogonal Fusion of Local and Global Features | [
"Min Yang",
"Dongliang He",
"Miao Fan",
"Baorong Shi",
"Xuetong Xue",
"Fu Li",
"Errui Ding",
"Jizhou Huang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_DOLG_Single-Stage_Image_Retrieval_With_Deep_Orthogonal_Fusion_of_Local_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_DOLG_Single-Stage_Image_Retrieval_With_Deep_Orthogonal_Fusion_of_Local_ICCV_2021_paper.pdf | null | 2108.02927 | cvf | @InProceedings{Yang_2021_ICCV,
author = {Yang, Min and He, Dongliang and Fan, Miao and Shi, Baorong and Xue, Xuetong and Li, Fu and Ding, Errui and Huang, Jizhou},
title = {DOLG: Single-Stage Image Retrieval With Deep Orthogonal Fusion of Local and Global Features},
booktitle = {Proceedings of the IE... | Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based stud... | [
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25 | Light Source Guided Single-Image Flare Removal From Unpaired Data | [
"Xiaotian Qiao",
"Gerhard P. Hancke",
"Rynson W.H. Lau"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Qiao_2021_ICCV,
author = {Qiao, Xiaotian and Hancke, Gerhard P. and Lau, Rynson W.H.},
title = {Light Source Guided Single-Image Flare Removal From Unpaired Data},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined i... | [
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26 | Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization | [
"Wei Zhu",
"Haitian Zheng",
"Haofu Liao",
"Weijian Li",
"Jiebo Luo"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhu_Learning_Bias-Invariant_Representation_by_Cross-Sample_Mutual_Information_Minimization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhu_Learning_Bias-Invariant_Representation_ICCV_2021_supplemental.pdf | 2108.05449 | cvf | @InProceedings{Zhu_2021_ICCV,
author = {Zhu, Wei and Zheng, Haitian and Liao, Haofu and Li, Weijian and Luo, Jiebo},
title = {Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (... | Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with ... | [
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27 | Selective Feature Compression for Efficient Activity Recognition Inference | [
"Chunhui Liu",
"Xinyu Li",
"Hao Chen",
"Davide Modolo",
"Joseph Tighe"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Selective_Feature_Compression_for_Efficient_Activity_Recognition_Inference_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Selective_Feature_Compression_for_Efficient_Activity_Recognition_Inference_ICCV_2021_paper.pdf | null | 2104.00179 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Chunhui and Li, Xinyu and Chen, Hao and Modolo, Davide and Tighe, Joseph},
title = {Selective Feature Compression for Efficient Activity Recognition Inference},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world application. In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustra... | [
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28 | Attention-Based Multi-Reference Learning for Image Super-Resolution | [
"Marco Pesavento",
"Marco Volino",
"Adrian Hilton"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Pesavento_Attention-Based_Multi-Reference_Learning_for_Image_Super-Resolution_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Pesavento_Attention-Based_Multi-Reference_Learning_for_Image_Super-Resolution_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Pesavento_Attention-Based_Multi-Reference_Learning_ICCV_2021_supplemental.pdf | 2108.13697 | cvf | @InProceedings{Pesavento_2021_ICCV,
author = {Pesavento, Marco and Volino, Marco and Hilton, Adrian},
title = {Attention-Based Multi-Reference Learning for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},... | This paper proposes a novel Attention-based Multi-Reference Super-resolution network (AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar texture from multiple reference images to the super-resolution output whilst maintaining spatial coherence. The use of multiple reference images... | [
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29 | Spatial-Temporal Transformer for Dynamic Scene Graph Generation | [
"Yuren Cong",
"Wentong Liao",
"Hanno Ackermann",
"Bodo Rosenhahn",
"Michael Ying Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cong_Spatial-Temporal_Transformer_for_Dynamic_Scene_Graph_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cong_Spatial-Temporal_Transformer_for_Dynamic_Scene_Graph_Generation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Cong_Spatial-Temporal_Transformer_for_ICCV_2021_supplemental.pdf | 2107.12309 | cvf | @InProceedings{Cong_2021_ICCV,
author = {Cong, Yuren and Liao, Wentong and Ackermann, Hanno and Rosenhahn, Bodo and Yang, Michael Ying},
title = {Spatial-Temporal Transformer for Dynamic Scene Graph Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICC... | Dynamic scene graph generation aims at generating a scene graph of the given video. Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation. In this ... | [
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30 | Deep Transport Network for Unsupervised Video Object Segmentation | [
"Kaihua Zhang",
"Zicheng Zhao",
"Dong Liu",
"Qingshan Liu",
"Bo Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Deep_Transport_Network_for_Unsupervised_Video_Object_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Deep_Transport_Network_for_Unsupervised_Video_Object_Segmentation_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Kaihua and Zhao, Zicheng and Liu, Dong and Liu, Qingshan and Liu, Bo},
title = {Deep Transport Network for Unsupervised Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network. However, they cannot handle the distracting noises in each input modality, which may vastly deteriorate the model performance. We propose to establish the correspondence between the input modalities ... | [
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31 | RDI-Net: Relational Dynamic Inference Networks | [
"Huanyu Wang",
"Songyuan Li",
"Shihao Su",
"Zequn Qin",
"Xi Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_RDI-Net_Relational_Dynamic_Inference_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_RDI-Net_Relational_Dynamic_Inference_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_RDI-Net_Relational_Dynamic_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_ICCV,
author = {Wang, Huanyu and Li, Songyuan and Su, Shihao and Qin, Zequn and Li, Xi},
title = {RDI-Net: Relational Dynamic Inference Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year... | Dynamic inference networks, aimed at promoting computational efficiency, go along an adaptive executing path for a given sample. Prevalent methods typically assign a router for each convolutional block and sequentially make block-by-block executing decisions, without considering the relations during the dynamic inferen... | [
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32 | Densely Guided Knowledge Distillation Using Multiple Teacher Assistants | [
"Wonchul Son",
"Jaemin Na",
"Junyong Choi",
"Wonjun Hwang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Son_Densely_Guided_Knowledge_Distillation_Using_Multiple_Teacher_Assistants_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Son_Densely_Guided_Knowledge_Distillation_Using_Multiple_Teacher_Assistants_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Son_Densely_Guided_Knowledge_ICCV_2021_supplemental.pdf | 2009.08825 | cvf | @InProceedings{Son_2021_ICCV,
author = {Son, Wonchul and Na, Jaemin and Choi, Junyong and Hwang, Wonjun},
title = {Densely Guided Knowledge Distillation Using Multiple Teacher Assistants},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for model compression and transfer learning. However, few studies have been performed to resolve the poor learning issue of the student network whe... | [
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33 | Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift | [
"Jiefeng Peng",
"Jiqi Zhang",
"Changlin Li",
"Guangrun Wang",
"Xiaodan Liang",
"Liang Lin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Peng_Pi-NAS_Improving_Neural_ICCV_2021_supplemental.pdf | 2108.09671 | title_snapshot | @InProceedings{Peng_2021_ICCV,
author = {Peng, Jiefeng and Zhang, Jiqi and Li, Changlin and Wang, Guangrun and Liang, Xiaodan and Lin, Liang},
title = {Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift},
booktitle = {Proceedings of the IEEE/CVF International... | Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached from the supernet. However, the ranking correlation between the architectures' predicted accuracy and their actual capability is incorrect,... | [
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34 | ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators | [
"Qixing Huang",
"Xiangru Huang",
"Bo Sun",
"Zaiwei Zhang",
"Junfeng Jiang",
"Chandrajit Bajaj"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_ARAPReg_An_As-Rigid-As_Possible_Regularization_Loss_for_Learning_Deformable_Shape_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_ARAPReg_An_As-Rigid-As_Possible_Regularization_Loss_for_Learning_Deformable_Shape_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_ARAPReg_An_As-Rigid-As_ICCV_2021_supplemental.pdf | 2108.09432 | cvf | @InProceedings{Huang_2021_ICCV,
author = {Huang, Qixing and Huang, Xiangru and Sun, Bo and Zhang, Zaiwei and Jiang, Junfeng and Bajaj, Chandrajit},
title = {ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators},
booktitle = {Proceedings of the IEEE/CVF Interna... | This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on a local approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the uns... | [
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35 | Online Refinement of Low-Level Feature Based Activation Map for Weakly Supervised Object Localization | [
"Jinheng Xie",
"Cheng Luo",
"Xiangping Zhu",
"Ziqi Jin",
"Weizeng Lu",
"Linlin Shen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xie_Online_Refinement_of_Low-Level_Feature_Based_Activation_Map_for_Weakly_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Online_Refinement_of_Low-Level_Feature_Based_Activation_Map_for_Weakly_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xie_Online_Refinement_of_ICCV_2021_supplemental.pdf | 2110.05741 | cvf | @InProceedings{Xie_2021_ICCV,
author = {Xie, Jinheng and Luo, Cheng and Zhu, Xiangping and Jin, Ziqi and Lu, Weizeng and Shen, Linlin},
title = {Online Refinement of Low-Level Feature Based Activation Map for Weakly Supervised Object Localization},
booktitle = {Proceedings of the IEEE/CVF Internation... | We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objects using the low-level feature based activation maps. In the first stage, an activation map generato... | [
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36 | Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection | [
"Markos Diomataris",
"Nikolaos Gkanatsios",
"Vassilis Pitsikalis",
"Petros Maragos"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Diomataris_Grounding_Consistency_Distilling_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Diomataris_2021_ICCV,
author = {Diomataris, Markos and Gkanatsios, Nikolaos and Pitsikalis, Vassilis and Maragos, Petros},
title = {Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection},
booktitle = {Proceedings of the IEEE/CVF International ... | Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet. Most SGGs silently exploit datasets' bias on relationships' context, i.e. its subject and object, to improve recall and neglect spatial and visual evidence, e.g.... | [
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37 | Long-Term Temporally Consistent Unpaired Video Translation From Simulated Surgical 3D Data | [
"Dominik Rivoir",
"Micha Pfeiffer",
"Reuben Docea",
"Fiona Kolbinger",
"Carina Riediger",
"Jürgen Weitz",
"Stefanie Speidel"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Rivoir_Long-Term_Temporally_Consistent_Unpaired_Video_Translation_From_Simulated_Surgical_3D_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Rivoir_Long-Term_Temporally_Consistent_Unpaired_Video_Translation_From_Simulated_Surgical_3D_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Rivoir_Long-Term_Temporally_Consistent_ICCV_2021_supplemental.pdf | 2103.17204 | cvf | @InProceedings{Rivoir_2021_ICCV,
author = {Rivoir, Dominik and Pfeiffer, Micha and Docea, Reuben and Kolbinger, Fiona and Riediger, Carina and Weitz, J\"urgen and Speidel, Stefanie},
title = {Long-Term Temporally Consistent Unpaired Video Translation From Simulated Surgical 3D Data},
booktitle = {Pro... | Research in unpaired video translation has mainly focused on short-term temporal consistency by conditioning on neighboring frames. However for transfer from simulated to photorealistic sequences, available information on the underlying geometry offers potential for achieving global consistency across views. We propose... | [
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38 | Bridging the Gap Between Label- and Reference-Based Synthesis in Multi-Attribute Image-to-Image Translation | [
"Qiusheng Huang",
"Zhilin Zheng",
"Xueqi Hu",
"Li Sun",
"Qingli Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_Bridging_the_Gap_Between_Label-_and_Reference-Based_Synthesis_in_Multi-Attribute_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_Bridging_the_Gap_Between_Label-_and_Reference-Based_Synthesis_in_Multi-Attribute_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_Bridging_the_Gap_ICCV_2021_supplemental.pdf | 2110.05055 | title_snapshot | @InProceedings{Huang_2021_ICCV,
author = {Huang, Qiusheng and Zheng, Zhilin and Hu, Xueqi and Sun, Li and Li, Qingli},
title = {Bridging the Gap Between Label- and Reference-Based Synthesis in Multi-Attribute Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF International Conferen... | The image-to-image translation (I2IT) model takes a target label or a reference image as the input, and changes a source into the specified target domain. The two types of synthesis, either label- or reference-based, have substantial differences. Particularly, the label-based synthesis reflects the common characteristi... | [
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39 | A Broad Study on the Transferability of Visual Representations With Contrastive Learning | [
"Ashraful Islam",
"Chun-Fu (Richard) Chen",
"Rameswar Panda",
"Leonid Karlinsky",
"Richard Radke",
"Rogerio Feris"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Islam_A_Broad_Study_on_the_Transferability_of_Visual_Representations_With_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Islam_A_Broad_Study_on_the_Transferability_of_Visual_Representations_With_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Islam_A_Broad_Study_ICCV_2021_supplemental.pdf | 2103.13517 | cvf | @InProceedings{Islam_2021_ICCV,
author = {Islam, Ashraful and Chen, Chun-Fu (Richard) and Panda, Rameswar and Karlinsky, Leonid and Radke, Richard and Feris, Rogerio},
title = {A Broad Study on the Transferability of Visual Representations With Contrastive Learning},
booktitle = {Proceedings of the I... | Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods. Supervised contrastive learning has also been shown to outperform its cross-entropy counterparts by leveraging labels for choosing where to contrast. However, there has b... | [
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40 | TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series | [
"Yunsong Zhou",
"Hongzi Zhu",
"Chunqin Li",
"Tiankai Cui",
"Shan Chang",
"Minyi Guo"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_TempNet_Online_Semantic_Segmentation_on_Large-Scale_Point_Cloud_Series_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_TempNet_Online_Semantic_Segmentation_on_Large-Scale_Point_Cloud_Series_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Yunsong and Zhu, Hongzi and Li, Chunqin and Cui, Tiankai and Chang, Shan and Guo, Minyi},
title = {TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Online semantic segmentation on a time series of point cloud frames is an essential task in autonomous driving. Existing models focus on single-frame segmentation, which cannot achieve satisfactory segmentation accuracy and offer unstably flicker among frames. In this paper, we propose a light-weight semantic segmentat... | [
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41 | Bayesian Deep Basis Fitting for Depth Completion With Uncertainty | [
"Chao Qu",
"Wenxin Liu",
"Camillo J. Taylor"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Qu_Bayesian_Deep_Basis_Fitting_for_Depth_Completion_With_Uncertainty_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Qu_Bayesian_Deep_Basis_Fitting_for_Depth_Completion_With_Uncertainty_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Qu_Bayesian_Deep_Basis_ICCV_2021_supplemental.pdf | 2103.15254 | cvf | @InProceedings{Qu_2021_ICCV,
author = {Qu, Chao and Liu, Wenxin and Taylor, Camillo J.},
title = {Bayesian Deep Basis Fitting for Depth Completion With Uncertainty},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | In this work we investigate the problem of uncertainty estimation for image-guided depth completion. We extend Deep Basis Fitting (DBF) for depth completion within a Bayesian evidence framework to provide calibrated per-pixel variance. The DBF approach frames the depth completion problem in terms of a network that prod... | [
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42 | Query Adaptive Few-Shot Object Detection With Heterogeneous Graph Convolutional Networks | [
"Guangxing Han",
"Yicheng He",
"Shiyuan Huang",
"Jiawei Ma",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Han_Query_Adaptive_Few-Shot_Object_Detection_With_Heterogeneous_Graph_Convolutional_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Han_Query_Adaptive_Few-Shot_Object_Detection_With_Heterogeneous_Graph_Convolutional_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Han_Query_Adaptive_Few-Shot_ICCV_2021_supplemental.pdf | 2112.09791 | title_snapshot | @InProceedings{Han_2021_ICCV,
author = {Han, Guangxing and He, Yicheng and Huang, Shiyuan and Ma, Jiawei and Chang, Shih-Fu},
title = {Query Adaptive Few-Shot Object Detection With Heterogeneous Graph Convolutional Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. This field sees recent improvement owing to the meta-learning techniques by learning how to match between the query image and few-shot class examples, such that the learned model can generalize to few-shot novel classes. However, cur... | [
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43 | ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting | [
"Xiaohan Ding",
"Tianxiang Hao",
"Jianchao Tan",
"Ji Liu",
"Jungong Han",
"Yuchen Guo",
"Guiguang Ding"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ding_ResRep_Lossless_CNN_Pruning_via_Decoupling_Remembering_and_Forgetting_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ding_ResRep_Lossless_CNN_Pruning_via_Decoupling_Remembering_and_Forgetting_ICCV_2021_paper.pdf | null | 2007.03260 | cvf | @InProceedings{Ding_2021_ICCV,
author = {Ding, Xiaohan and Hao, Tianxiang and Tan, Jianchao and Liu, Ji and Han, Jungong and Guo, Yuchen and Ding, Guiguang},
title = {ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting},
booktitle = {Proceedings of the IEEE/CVF International Confer... | We propose ResRep, a novel method for lossless channel pruning (a.k.a. filter pruning), which slims down a CNN by reducing the width (number of output channels) of convolutional layers. Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into t... | [
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44 | P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching | [
"Bing Wang",
"Changhao Chen",
"Zhaopeng Cui",
"Jie Qin",
"Chris Xiaoxuan Lu",
"Zhengdi Yu",
"Peijun Zhao",
"Zhen Dong",
"Fan Zhu",
"Niki Trigoni",
"Andrew Markham"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_P2-Net_Joint_Description_and_Detection_of_Local_Features_for_Pixel_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_P2-Net_Joint_Description_and_Detection_of_Local_Features_for_Pixel_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_P2-Net_Joint_Description_ICCV_2021_supplemental.pdf | 2103.01055 | title_snapshot | @InProceedings{Wang_2021_ICCV,
author = {Wang, Bing and Chen, Changhao and Cui, Zhaopeng and Qin, Jie and Lu, Chris Xiaoxuan and Yu, Zhengdi and Zhao, Peijun and Dong, Zhen and Zhu, Fan and Trigoni, Niki and Markham, Andrew},
title = {P2-Net: Joint Description and Detection of Local Features for Pixel an... | Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D local feature descriptors and detectors having been proposed, the derivation of a shared descriptor and joint keypoint detector that directly m... | [
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45 | Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation | [
"Jogendra Nath Kundu",
"Akshay Kulkarni",
"Amit Singh",
"Varun Jampani",
"R. Venkatesh Babu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kundu_Generalize_Then_Adapt_Source-Free_Domain_Adaptive_Semantic_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kundu_Generalize_Then_Adapt_Source-Free_Domain_Adaptive_Semantic_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kundu_Generalize_Then_Adapt_ICCV_2021_supplemental.pdf | 2108.11249 | cvf | @InProceedings{Kundu_2021_ICCV,
author = {Kundu, Jogendra Nath and Kulkarni, Akshay and Singh, Amit and Jampani, Varun and Babu, R. Venkatesh},
title = {Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comp... | Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation. However, almost all prior arts assume concurrent access to both labeled source and unlabeled target, making them unsuitable for scenarios demanding source-free adaptation. In this work, we enable source-free DA by partitioning... | [
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46 | Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation | [
"Xin Hao",
"Sanyuan Zhao",
"Mang Ye",
"Jianbing Shen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hao_Cross-Modality_Person_Re-Identification_via_Modality_Confusion_and_Center_Aggregation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hao_Cross-Modality_Person_Re-Identification_via_Modality_Confusion_and_Center_Aggregation_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Hao_2021_ICCV,
author = {Hao, Xin and Zhao, Sanyuan and Ye, Mang and Shen, Jianbing},
title = {Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month... | Cross-modality person re-identification is a challenging task due to large cross-modality discrepancy and intra-modality variations. Currently, most existing methods focus on learning modality-specific or modality-shareable features by using the identity supervision or modality label. Different from existing methods, t... | [
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47 | T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning | [
"Zhen Zhong",
"Guobao Xiao",
"Linxin Zheng",
"Yan Lu",
"Jiayi Ma"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhong_T-Net_Effective_Permutation-Equivariant_Network_for_Two-View_Correspondence_Learning_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhong_2021_ICCV,
author = {Zhong, Zhen and Xiao, Guobao and Zheng, Linxin and Lu, Yan and Ma, Jiayi},
title = {T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (I... | We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: "-" s... | [
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48 | Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion | [
"Qinghao Ye",
"Xiyue Shen",
"Yuan Gao",
"Zirui Wang",
"Qi Bi",
"Ping Li",
"Guang Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ye_Temporal_Cue_Guided_Video_Highlight_Detection_With_Low-Rank_Audio-Visual_Fusion_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ye_Temporal_Cue_Guided_Video_Highlight_Detection_With_Low-Rank_Audio-Visual_Fusion_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ye_Temporal_Cue_Guided_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Ye_2021_ICCV,
author = {Ye, Qinghao and Shen, Xiyue and Gao, Yuan and Wang, Zirui and Bi, Qi and Li, Ping and Yang, Guang},
title = {Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Co... | Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i.e., where the highlight moments are in long videos) for supervised learning. In t... | [
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49 | Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models | [
"Linjie Li",
"Jie Lei",
"Zhe Gan",
"Jingjing Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Adversarial_VQA_A_New_Benchmark_for_Evaluating_the_Robustness_of_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Adversarial_VQA_A_New_Benchmark_for_Evaluating_the_Robustness_of_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Adversarial_VQA_A_ICCV_2021_supplemental.pdf | 2106.00245 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Linjie and Lei, Jie and Gan, Zhe and Liu, Jingjing},
title = {Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octobe... | Benefiting from large-scale pre-training, we have witnessed significant performance boost on the popular Visual Question Answering (VQA) task. Despite rapid progress, it remains unclear whether these state-of-the-art (SOTA) models are robust when encountering examples in the wild. To study this, we introduce Adversaria... | [
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50 | S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation | [
"Harsh Rangwani",
"Arihant Jain",
"Sumukh K Aithal",
"R. Venkatesh Babu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Rangwani_S3VAADA_Submodular_Subset_Selection_for_Virtual_Adversarial_Active_Domain_Adaptation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Rangwani_S3VAADA_Submodular_Subset_Selection_for_Virtual_Adversarial_Active_Domain_Adaptation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Rangwani_S3VAADA_Submodular_Subset_ICCV_2021_supplemental.zip | 2109.08901 | title_judge | @InProceedings{Rangwani_2021_ICCV,
author = {Rangwani, Harsh and Jain, Arihant and Aithal, Sumukh K and Babu, R. Venkatesh},
title = {S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer V... | Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario's it might be feasible to get labels for a small proportion of target data. In these sce... | [
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51 | Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation | [
"Jiabo Huang",
"Yang Liu",
"Shaogang Gong",
"Hailin Jin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_Cross-Sentence_Temporal_and_Semantic_Relations_in_Video_Activity_Localisation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_Cross-Sentence_Temporal_and_Semantic_Relations_in_Video_Activity_Localisation_ICCV_2021_paper.pdf | null | 2107.11443 | cvf | @InProceedings{Huang_2021_ICCV,
author = {Huang, Jiabo and Liu, Yang and Gong, Shaogang and Jin, Hailin},
title = {Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos. For supervised model training, a temporal annotation of both th... | [
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52 | StructDepth: Leveraging the Structural Regularities for Self-Supervised Indoor Depth Estimation | [
"Boying Li",
"Yuan Huang",
"Zeyu Liu",
"Danping Zou",
"Wenxian Yu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_StructDepth_Leveraging_the_Structural_Regularities_for_Self-Supervised_Indoor_Depth_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_StructDepth_Leveraging_the_Structural_Regularities_for_Self-Supervised_Indoor_Depth_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_StructDepth_Leveraging_the_ICCV_2021_supplemental.pdf | 2108.08574 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Boying and Huang, Yuan and Liu, Zeyu and Zou, Danping and Yu, Wenxian},
title = {StructDepth: Leveraging the Structural Regularities for Self-Supervised Indoor Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer V... | Self-supervised monocular depth estimation has achieved impressive performance on outdoor datasets. Its performance however degrades notably in indoor environments because of the lack of textures. Without rich textures, the photometric consistency is too weak to train a good depth network. Inspired by the early works o... | [
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53 | Feature Interactive Representation for Point Cloud Registration | [
"Bingli Wu",
"Jie Ma",
"Gaojie Chen",
"Pei An"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wu_Feature_Interactive_Representation_for_Point_Cloud_Registration_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Feature_Interactive_Representation_for_Point_Cloud_Registration_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Wu_2021_ICCV,
author = {Wu, Bingli and Ma, Jie and Chen, Gaojie and An, Pei},
title = {Feature Interactive Representation for Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Point cloud registration is the process of using the common structures in two point clouds to splice them together. To find out these common structures and make these structures match more accurately, we investigate the direction of interacting information of the source and target point clouds. To this end, we propose ... | [
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54 | Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis | [
"Tiange Xiang",
"Chaoyi Zhang",
"Yang Song",
"Jianhui Yu",
"Weidong Cai"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xiang_Walk_in_the_Cloud_Learning_Curves_for_Point_Clouds_Shape_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xiang_Walk_in_the_Cloud_Learning_Curves_for_Point_Clouds_Shape_ICCV_2021_paper.pdf | null | 2105.01288 | cvf | @InProceedings{Xiang_2021_ICCV,
author = {Xiang, Tiange and Zhang, Chaoyi and Song, Yang and Yu, Jianhui and Cai, Weidong},
title = {Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
m... | Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of connected points (curves) are initially grouped by taking guided walks in the point clouds, and then subsequently aggregated back to... | [
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55 | LSG-CPD: Coherent Point Drift With Local Surface Geometry for Point Cloud Registration | [
"Weixiao Liu",
"Hongtao Wu",
"Gregory S. Chirikjian"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_LSG-CPD_Coherent_Point_Drift_With_Local_Surface_Geometry_for_Point_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_LSG-CPD_Coherent_Point_Drift_With_Local_Surface_Geometry_for_Point_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_LSG-CPD_Coherent_Point_ICCV_2021_supplemental.pdf | 2103.15039 | title_snapshot | @InProceedings{Liu_2021_ICCV,
author = {Liu, Weixiao and Wu, Hongtao and Chirikjian, Gregory S.},
title = {LSG-CPD: Coherent Point Drift With Local Surface Geometry for Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Probabilistic point cloud registration methods are becoming more popular because of their robustness. However, unlike point-to-plane variants of iterative closest point (ICP) which incorporate local surface geometric information such as surface normals, most probabilistic methods (e.g., coherent point drift (CPD)) igno... | [
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56 | ISD: Self-Supervised Learning by Iterative Similarity Distillation | [
"Ajinkya Tejankar",
"Soroush Abbasi Koohpayegani",
"Vipin Pillai",
"Paolo Favaro",
"Hamed Pirsiavash"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Tejankar_ISD_Self-Supervised_Learning_by_Iterative_Similarity_Distillation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Tejankar_ISD_Self-Supervised_Learning_by_Iterative_Similarity_Distillation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Tejankar_ISD_Self-Supervised_Learning_ICCV_2021_supplemental.pdf | 2012.09259 | cvf | @InProceedings{Tejankar_2021_ICCV,
author = {Tejankar, Ajinkya and Koohpayegani, Soroush Abbasi and Pillai, Vipin and Favaro, Paolo and Pirsiavash, Hamed},
title = {ISD: Self-Supervised Learning by Iterative Similarity Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference o... | Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to pull two augmentations of an image (positive pairs) closer compared to other random images (negative pairs). We argue that not all negative images are equally negative. Hence, we introduce a self-supervised ... | [
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57 | An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation | [
"Rongchang Xie",
"Chunyu Wang",
"Wenjun Zeng",
"Yizhou Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xie_An_Empirical_Study_ICCV_2021_supplemental.pdf | 2011.12498 | cvf | @InProceedings{Xie_2021_ICCV,
author = {Xie, Rongchang and Wang, Chunyu and Zeng, Wenjun and Wang, Yizhou},
title = {An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | The state-of-the-art semi-supervised learning models are consistency-based which learn about unlabeled images by maximizing the similarity between different augmentations of an image. But when we apply the methods to human pose estimation which has extremely imbalanced class distribution, the models often collapse and ... | [
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58 | Self-Supervised Neural Networks for Spectral Snapshot Compressive Imaging | [
"Ziyi Meng",
"Zhenming Yu",
"Kun Xu",
"Xin Yuan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Meng_Self-Supervised_Neural_Networks_for_Spectral_Snapshot_Compressive_Imaging_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Meng_Self-Supervised_Neural_Networks_for_Spectral_Snapshot_Compressive_Imaging_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Meng_Self-Supervised_Neural_Networks_ICCV_2021_supplemental.pdf | 2108.12654 | cvf | @InProceedings{Meng_2021_ICCV,
author = {Meng, Ziyi and Yu, Zhenming and Xu, Kun and Yuan, Xin},
title = {Self-Supervised Neural Networks for Spectral Snapshot Compressive Imaging},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October}... | We consider using untrained neural networks to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Various SCI systems have been built in recent years to capture data such as high-... | [
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59 | Group-Aware Contrastive Regression for Action Quality Assessment | [
"Xumin Yu",
"Yongming Rao",
"Wenliang Zhao",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yu_Group-Aware_Contrastive_Regression_for_Action_Quality_Assessment_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yu_Group-Aware_Contrastive_Regression_for_Action_Quality_Assessment_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yu_Group-Aware_Contrastive_Regression_ICCV_2021_supplemental.pdf | 2108.07797 | cvf | @InProceedings{Yu_2021_ICCV,
author = {Yu, Xumin and Rao, Yongming and Zhao, Wenliang and Lu, Jiwen and Zhou, Jie},
title = {Group-Aware Contrastive Regression for Action Quality Assessment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month =... | Assessing action quality is challenging due to the subtle differences between videos and large variations in scores. Most existing approaches tackle this problem by regressing a quality score from a single video, suffering a lot from the large inter-video score variations. In this paper, we show that the relations amon... | [
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60 | The Road To Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation | [
"Yuankai Qi",
"Zizheng Pan",
"Yicong Hong",
"Ming-Hsuan Yang",
"Anton van den Hengel",
"Qi Wu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Qi_The_Road_To_Know-Where_An_Object-and-Room_Informed_Sequential_BERT_for_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Qi_The_Road_To_Know-Where_An_Object-and-Room_Informed_Sequential_BERT_for_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Qi_The_Road_To_ICCV_2021_supplemental.pdf | 2104.04167 | title_snapshot | @InProceedings{Qi_2021_ICCV,
author = {Qi, Yuankai and Pan, Zizheng and Hong, Yicong and Yang, Ming-Hsuan and van den Hengel, Anton and Wu, Qi},
title = {The Road To Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation},
booktitle = {Proceedings of the IEEE/CV... | Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this... | [
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61 | Support-Set Based Cross-Supervision for Video Grounding | [
"Xinpeng Ding",
"Nannan Wang",
"Shiwei Zhang",
"De Cheng",
"Xiaomeng Li",
"Ziyuan Huang",
"Mingqian Tang",
"Xinbo Gao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ding_Support-Set_Based_Cross-Supervision_ICCV_2021_supplemental.pdf | 2108.10576 | cvf | @InProceedings{Ding_2021_ICCV,
author = {Ding, Xinpeng and Wang, Nannan and Zhang, Shiwei and Cheng, De and Li, Xiaomeng and Huang, Ziyuan and Tang, Mingqian and Gao, Xinbo},
title = {Support-Set Based Cross-Supervision for Video Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conf... | Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to learn the complicated multi-modal relations by only architecture designing in fact. In this paper, we introduce a novel Support-set Based C... | [
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62 | Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration | [
"Haobo Jiang",
"Yaqi Shen",
"Jin Xie",
"Jun Li",
"Jianjun Qian",
"Jian Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jiang_Sampling_Network_Guided_Cross-Entropy_Method_for_Unsupervised_Point_Cloud_Registration_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jiang_Sampling_Network_Guided_Cross-Entropy_Method_for_Unsupervised_Point_Cloud_Registration_ICCV_2021_paper.pdf | null | 2109.06619 | cvf | @InProceedings{Jiang_2021_ICCV,
author = {Jiang, Haobo and Shen, Yaqi and Xie, Jin and Li, Jun and Qian, Jianjun and Yang, Jian},
title = {Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comp... | In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, g... | [
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63 | Voxel-Based Network for Shape Completion by Leveraging Edge Generation | [
"Xiaogang Wang",
"Marcelo H Ang",
"Gim Hee Lee"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Voxel-Based_Network_for_ICCV_2021_supplemental.pdf | 2108.09936 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Xiaogang and Ang, Marcelo H and Lee, Gim Hee},
title = {Voxel-Based Network for Shape Completion by Leveraging Edge Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of fine-grained details. In this paper, we develop a voxel-based network ... | [
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64 | THUNDR: Transformer-Based 3D Human Reconstruction With Markers | [
"Mihai Zanfir",
"Andrei Zanfir",
"Eduard Gabriel Bazavan",
"William T. Freeman",
"Rahul Sukthankar",
"Cristian Sminchisescu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zanfir_THUNDR_Transformer-Based_3D_Human_Reconstruction_With_Markers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zanfir_THUNDR_Transformer-Based_3D_Human_Reconstruction_With_Markers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zanfir_THUNDR_Transformer-Based_3D_ICCV_2021_supplemental.pdf | 2106.09336 | cvf | @InProceedings{Zanfir_2021_ICCV,
author = {Zanfir, Mihai and Zanfir, Andrei and Bazavan, Eduard Gabriel and Freeman, William T. and Sukthankar, Rahul and Sminchisescu, Cristian},
title = {THUNDR: Transformer-Based 3D Human Reconstruction With Markers},
booktitle = {Proceedings of the IEEE/CVF Interna... | We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an intermediate 3d marker representation, where we aim to combine the predictive power of model-free-output architectures and the regularizing, ant... | [
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65 | OadTR: Online Action Detection With Transformers | [
"Xiang Wang",
"Shiwei Zhang",
"Zhiwu Qing",
"Yuanjie Shao",
"Zhengrong Zuo",
"Changxin Gao",
"Nong Sang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_OadTR_Online_Action_Detection_With_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_OadTR_Online_Action_Detection_With_Transformers_ICCV_2021_paper.pdf | null | 2106.11149 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Xiang and Zhang, Shiwei and Qing, Zhiwu and Shao, Yuanjie and Zuo, Zhengrong and Gao, Changxin and Sang, Nong},
title = {OadTR: Online Action Detection With Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism and gradient vanishing, hence it is hard to be optimized. In this paper, we propose a new encoder-decoder framework based on Transformers, n... | [
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66 | Instance-Level Image Retrieval Using Reranking Transformers | [
"Fuwen Tan",
"Jiangbo Yuan",
"Vicente Ordonez"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Tan_Instance-Level_Image_Retrieval_Using_Reranking_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Tan_Instance-Level_Image_Retrieval_Using_Reranking_Transformers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Tan_Instance-Level_Image_Retrieval_ICCV_2021_supplemental.zip | 2103.12236 | cvf | @InProceedings{Tan_2021_ICCV,
author = {Tan, Fuwen and Yuan, Jiangbo and Ordonez, Vicente},
title = {Instance-Level Image Retrieval Using Reranking Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {... | Instance-level image retrieval is the task of searching in a large database for images that match an object in a query image. To address this task, systems usually rely on a retrieval step that uses global image descriptors, and a subsequent step that performs domain-specific refinements or reranking by leveraging oper... | [
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67 | Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image | [
"Zunlei Feng",
"Zhonghua Wang",
"Xinchao Wang",
"Yining Mao",
"Thomas Li",
"Jie Lei",
"Yuexuan Wang",
"Mingli Song"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Feng_Mutual-Complementing_Framework_for_Nuclei_Detection_and_Segmentation_in_Pathology_Image_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Feng_Mutual-Complementing_Framework_for_Nuclei_Detection_and_Segmentation_in_Pathology_Image_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Feng_Mutual-Complementing_Framework_for_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Feng_2021_ICCV,
author = {Feng, Zunlei and Wang, Zhonghua and Wang, Xinchao and Mao, Yining and Li, Thomas and Lei, Jie and Wang, Yuexuan and Song, Mingli},
title = {Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image},
booktitle = {Proceedings of th... | Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What's more, accurate segmentation detection of nuclei can be challenging due to the... | [
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68 | Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform | [
"Zhiyuan Mao",
"Nicholas Chimitt",
"Stanley H. Chan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Mao_Accelerating_Atmospheric_Turbulence_Simulation_via_Learned_Phase-to-Space_Transform_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Mao_Accelerating_Atmospheric_Turbulence_Simulation_via_Learned_Phase-to-Space_Transform_ICCV_2021_paper.pdf | null | 2107.11627 | cvf | @InProceedings{Mao_2021_ICCV,
author = {Mao, Zhiyuan and Chimitt, Nicholas and Chan, Stanley H.},
title = {Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms. Recognizing the limitations of previous approaches, we introduce a new concept known as the phase-to-space (P2S) transform to significantly speed up the simulation. P2S is built upon thre... | [
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69 | Graph Constrained Data Representation Learning for Human Motion Segmentation | [
"Mariella Dimiccoli",
"Lluís Garrido",
"Guillem Rodriguez-Corominas",
"Herwig Wendt"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Dimiccoli_Graph_Constrained_Data_Representation_Learning_for_Human_Motion_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Dimiccoli_Graph_Constrained_Data_Representation_Learning_for_Human_Motion_Segmentation_ICCV_2021_paper.pdf | null | 2107.13362 | title_snapshot | @InProceedings{Dimiccoli_2021_ICCV,
author = {Dimiccoli, Mariella and Garrido, Llu{\'\i}s and Rodriguez-Corominas, Guillem and Wendt, Herwig},
title = {Graph Constrained Data Representation Learning for Human Motion Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on C... | Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and curren... | [
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70 | Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition | [
"Jian Jia",
"Xiaotang Chen",
"Kaiqi Huang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.pdf | null | 2109.05686 | cvf | @InProceedings{Jia_2021_ICCV,
author = {Jia, Jian and Chen, Xiaotang and Huang, Kaiqi},
title = {Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octob... | While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial... | [
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71 | Learning To Stylize Novel Views | [
"Hsin-Ping Huang",
"Hung-Yu Tseng",
"Saurabh Saini",
"Maneesh Singh",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_Learning_To_Stylize_Novel_Views_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_Learning_To_Stylize_Novel_Views_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_Learning_To_Stylize_ICCV_2021_supplemental.pdf | 2105.13509 | cvf | @InProceedings{Huang_2021_ICCV,
author = {Huang, Hsin-Ping and Tseng, Hung-Yu and Saini, Saurabh and Singh, Maneesh and Yang, Ming-Hsuan},
title = {Learning To Stylize Novel Views},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October}... | We tackle a 3D scene stylization problem -- generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the desired style as inputs. Direct solution of combining novel view synthesis and stylization approaches lead to results that are blurry or not c... | [
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72 | Morphable Detector for Object Detection on Demand | [
"Xiangyun Zhao",
"Xu Zou",
"Ying Wu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Morphable_Detector_for_Object_Detection_on_Demand_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Morphable_Detector_for_Object_Detection_on_Demand_ICCV_2021_paper.pdf | null | 2110.04917 | cvf | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Xiangyun and Zou, Xu and Wu, Ying},
title = {Morphable Detector for Object Detection on Demand},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel categories on the fly with minimum online efforts. This is an object detection on demand (ODOD) task. It is challenging, because it is impossible to annotate large data on t... | [
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73 | Stacked Homography Transformations for Multi-View Pedestrian Detection | [
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"Jialian Wu",
"Ming Yang",
"Qian Zhang",
"Yuan Li",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Song_Stacked_Homography_Transformations_for_Multi-View_Pedestrian_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Stacked_Homography_Transformations_for_Multi-View_Pedestrian_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Song_Stacked_Homography_Transformations_ICCV_2021_supplemental.zip | null | null | @InProceedings{Song_2021_ICCV,
author = {Song, Liangchen and Wu, Jialian and Yang, Ming and Zhang, Qian and Li, Yuan and Yuan, Junsong},
title = {Stacked Homography Transformations for Multi-View Pedestrian Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Multi-view pedestrian detection aims to predict a bird's eye view (BEV) occupancy map from multiple camera views. This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views. In this paper, we propose a novel Stack... | [
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74 | Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments | [
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"Ziyi Bai",
"Xilin Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Gao_Env-QA_A_Video_Question_Answering_Benchmark_for_Comprehensive_Understanding_of_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Gao_Env-QA_A_Video_Question_Answering_Benchmark_for_Comprehensive_Understanding_of_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Gao_Env-QA_A_Video_ICCV_2021_supplemental.zip | null | null | @InProceedings{Gao_2021_ICCV,
author = {Gao, Difei and Wang, Ruiping and Bai, Ziyi and Chen, Xilin},
title = {Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (IC... | Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects' state changes, which are still challenging for current AI systems. T... | [
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75 | Region-Aware Contrastive Learning for Semantic Segmentation | [
"Hanzhe Hu",
"Jinshi Cui",
"Liwei Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hu_Region-Aware_Contrastive_Learning_for_Semantic_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_Region-Aware_Contrastive_Learning_for_Semantic_Segmentation_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Hu_2021_ICCV,
author = {Hu, Hanzhe and Cui, Jinshi and Wang, Liwei},
title = {Region-Aware Contrastive Learning for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
... | Recent works have made great success in semantic segmentation by exploiting contextual information in a local or global manner within individual image and supervising the model with pixel-wise cross entropy loss. However, from the holistic view of the whole dataset, semantic relations not only exist inside one single i... | [
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76 | Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models | [
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"Stephen Gould"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Image_Retrieval_on_Real-Life_Images_With_Pre-Trained_Vision-and-Language_Models_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Image_Retrieval_on_Real-Life_Images_With_Pre-Trained_Vision-and-Language_Models_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Image_Retrieval_on_ICCV_2021_supplemental.pdf | 2108.04024 | title_snapshot | @InProceedings{Liu_2021_ICCV,
author = {Liu, Zheyuan and Rodriguez-Opazo, Cristian and Teney, Damien and Gould, Stephen},
title = {Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (IC... | We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reason... | [
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77 | Self-Supervised Real-to-Sim Scene Generation | [
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"Jean-Francois Lafleche",
"Eric Cameracci",
"Gavriel State",
"Stan Birchfield",
"Marc T. Law"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Prakash_Self-Supervised_Real-to-Sim_Scene_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Prakash_Self-Supervised_Real-to-Sim_Scene_Generation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Prakash_Self-Supervised_Real-to-Sim_Scene_ICCV_2021_supplemental.pdf | 2011.14488 | cvf | @InProceedings{Prakash_2021_ICCV,
author = {Prakash, Aayush and Debnath, Shoubhik and Lafleche, Jean-Francois and Cameracci, Eric and State, Gavriel and Birchfield, Stan and Law, Marc T.},
title = {Self-Supervised Real-to-Sim Scene Generation},
booktitle = {Proceedings of the IEEE/CVF International C... | Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Synthetic data generation, however, can itself be prohibitively expensive when domain experts have to manually and painstakingly oversee the pr... | [
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78 | GP-S3Net: Graph-Based Panoptic Sparse Semantic Segmentation Network | [
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"Enxu Li",
"Ehsan Taghavi",
"Yuan Ren",
"Liu Bingbing"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Razani_GP-S3Net_Graph-Based_Panoptic_Sparse_Semantic_Segmentation_Network_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Razani_GP-S3Net_Graph-Based_Panoptic_Sparse_Semantic_Segmentation_Network_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Razani_GP-S3Net_Graph-Based_Panoptic_ICCV_2021_supplemental.zip | 2108.08401 | title_snapshot | @InProceedings{Razani_2021_ICCV,
author = {Razani, Ryan and Cheng, Ran and Li, Enxu and Taghavi, Ehsan and Ren, Yuan and Bingbing, Liu},
title = {GP-S3Net: Graph-Based Panoptic Sparse Semantic Segmentation Network},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision ... | Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In this paper, we propose a new computationally efficient LiDAR based panoptic segmentation framework, called GP-S3Net. GP-S3Net is a proposal-... | [
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79 | Learning From Noisy Data With Robust Representation Learning | [
"Junnan Li",
"Caiming Xiong",
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] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Junnan and Xiong, Caiming and Hoi, Steven C.H.},
title = {Learning From Noisy Data With Robust Representation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existi... | [
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80 | Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity | [
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"Guosheng Lin",
"Qingyao Wu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Su_Self-Supervised_3D_Skeleton_Action_Representation_Learning_With_Motion_Consistency_and_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Su_Self-Supervised_3D_Skeleton_Action_Representation_Learning_With_Motion_Consistency_and_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Su_Self-Supervised_3D_Skeleton_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Su_2021_ICCV,
author = {Su, Yukun and Lin, Guosheng and Wu, Qingyao},
title = {Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain. Yet, very little is explored about its usefulness in 3D skeleton-based action recognition understanding. Directly applying existing SSL techn... | [
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81 | Feature Importance-Aware Transferable Adversarial Attacks | [
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"Hengchang Guo",
"Zhifei Zhang",
"Wenxin Liu",
"Zhan Qin",
"Kui Ren"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Feature_Importance-Aware_Transferable_Adversarial_Attacks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Feature_Importance-Aware_Transferable_Adversarial_Attacks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Feature_Importance-Aware_Transferable_ICCV_2021_supplemental.pdf | 2107.14185 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Zhibo and Guo, Hengchang and Zhang, Zhifei and Liu, Wenxin and Qin, Zhan and Ren, Kui},
title = {Feature Importance-Aware Transferable Adversarial Attacks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., blackbox attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accur... | [
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82 | Exploring Classification Equilibrium in Long-Tailed Object Detection | [
"Chengjian Feng",
"Yujie Zhong",
"Weilin Huang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Feng_Exploring_Classification_Equilibrium_in_Long-Tailed_Object_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Feng_Exploring_Classification_Equilibrium_in_Long-Tailed_Object_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Feng_Exploring_Classification_Equilibrium_ICCV_2021_supplemental.pdf | 2108.07507 | cvf | @InProceedings{Feng_2021_ICCV,
author = {Feng, Chengjian and Zhong, Yujie and Huang, Weilin},
title = {Exploring Classification Equilibrium in Long-Tailed Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
ye... | The conventional detectors tend to make imbalanced classification and suffer performance drop, when the distribution of the training data is severely skewed. In this paper, we propose to use the mean classification score to indicate the classification accuracy for each category during training. Based on this indicator,... | [
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83 | Meta Gradient Adversarial Attack | [
"Zheng Yuan",
"Jie Zhang",
"Yunpei Jia",
"Chuanqi Tan",
"Tao Xue",
"Shiguang Shan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yuan_Meta_Gradient_Adversarial_Attack_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yuan_Meta_Gradient_Adversarial_Attack_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yuan_Meta_Gradient_Adversarial_ICCV_2021_supplemental.pdf | 2108.04204 | cvf | @InProceedings{Yuan_2021_ICCV,
author = {Yuan, Zheng and Zhang, Jie and Jia, Yunpei and Tan, Chuanqi and Xue, Tao and Shan, Shiguang},
title = {Meta Gradient Adversarial Attack},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper pr... | [
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84 | Differentiable Convolution Search for Point Cloud Processing | [
"Xing Nie",
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"Jianlong Chang",
"Chunlei Huo",
"Gaofeng Meng",
"Qi Tian",
"Weiming Hu",
"Chunhong Pan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Nie_Differentiable_Convolution_Search_for_Point_Cloud_Processing_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Nie_Differentiable_Convolution_Search_for_Point_Cloud_Processing_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Nie_Differentiable_Convolution_Search_ICCV_2021_supplemental.pdf | 2108.12856 | cvf | @InProceedings{Nie_2021_ICCV,
author = {Nie, Xing and Liu, Yongcheng and Chen, Shaohong and Chang, Jianlong and Huo, Chunlei and Meng, Gaofeng and Tian, Qi and Hu, Weiming and Pan, Chunhong},
title = {Differentiable Convolution Search for Point Cloud Processing},
booktitle = {Proceedings of the IEEE/... | Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds. To address these problems, many handcrafted convolution variants have sprung up in recent years. Though with elaborate design, these var... | [
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85 | Zero-Shot Day-Night Domain Adaptation With a Physics Prior | [
"Attila Lengyel",
"Sourav Garg",
"Michael Milford",
"Jan C. van Gemert"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Lengyel_Zero-Shot_Day-Night_Domain_Adaptation_With_a_Physics_Prior_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Lengyel_Zero-Shot_Day-Night_Domain_Adaptation_With_a_Physics_Prior_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Lengyel_Zero-Shot_Day-Night_Domain_ICCV_2021_supplemental.pdf | 2108.05137 | cvf | @InProceedings{Lengyel_2021_ICCV,
author = {Lengyel, Attila and Garg, Sourav and Milford, Michael and van Gemert, Jan C.},
title = {Zero-Shot Day-Night Domain Adaptation With a Physics Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | We explore the zero-shot setting for day-night domain adaptation. The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set. As gathering relevant test data is expensive and sometimes even impossible, we do not rely on test ... | [
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86 | Sketch Your Own GAN | [
"Sheng-Yu Wang",
"David Bau",
"Jun-Yan Zhu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Sketch_Your_Own_GAN_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Sketch_Your_Own_GAN_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Sketch_Your_Own_ICCV_2021_supplemental.pdf | 2108.02774 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Sheng-Yu and Bau, David and Zhu, Jun-Yan},
title = {Sketch Your Own GAN},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {14050-14060}
} | Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. In... | [
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87 | Minimal Solutions for Panoramic Stitching Given Gravity Prior | [
"Yaqing Ding",
"Daniel Barath",
"Zuzana Kukelova"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ding_Minimal_Solutions_for_ICCV_2021_supplemental.pdf | 2012.00465 | cvf | @InProceedings{Ding_2021_ICCV,
author = {Ding, Yaqing and Barath, Daniel and Kukelova, Zuzana},
title = {Minimal Solutions for Panoramic Stitching Given Gravity Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, e.g. smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras ca... | [
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88 | iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis | [
"Andreas Blattmann",
"Timo Milbich",
"Michael Dorkenwald",
"Björn Ommer"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Blattmann_iPOKE_Poking_a_Still_Image_for_Controlled_Stochastic_Video_Synthesis_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Blattmann_iPOKE_Poking_a_Still_Image_for_Controlled_Stochastic_Video_Synthesis_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Blattmann_iPOKE_Poking_a_ICCV_2021_supplemental.zip | 2107.02790 | cvf | @InProceedings{Blattmann_2021_ICCV,
author = {Blattmann, Andreas and Milbich, Timo and Dorkenwald, Michael and Ommer, Bj\"orn},
title = {iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate... | [
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89 | Neural Radiance Flow for 4D View Synthesis and Video Processing | [
"Yilun Du",
"Yinan Zhang",
"Hong-Xing Yu",
"Joshua B. Tenenbaum",
"Jiajun Wu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Du_Neural_Radiance_Flow_for_4D_View_Synthesis_and_Video_Processing_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Du_Neural_Radiance_Flow_for_4D_View_Synthesis_and_Video_Processing_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Du_Neural_Radiance_Flow_ICCV_2021_supplemental.zip | 2012.09790 | cvf | @InProceedings{Du_2021_ICCV,
author = {Du, Yilun and Zhang, Yinan and Yu, Hong-Xing and Tenenbaum, Joshua B. and Wu, Jiajun},
title = {Neural Radiance Flow for 4D View Synthesis and Video Processing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mo... | We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing consistency across... | [
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90 | Assignment-Space-Based Multi-Object Tracking and Segmentation | [
"Anwesa Choudhuri",
"Girish Chowdhary",
"Alexander G. Schwing"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Choudhuri_Assignment-Space-Based_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Choudhuri_Assignment-Space-Based_Multi-Object_Tracking_and_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Choudhuri_Assignment-Space-Based_Multi-Object_Tracking_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Choudhuri_2021_ICCV,
author = {Choudhuri, Anwesa and Chowdhary, Girish and Schwing, Alexander G.},
title = {Assignment-Space-Based Multi-Object Tracking and Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oct... | Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge. MOTS methods formulate tracking locally, i.e., frame-by... | [
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91 | Vi2CLR: Video and Image for Visual Contrastive Learning of Representation | [
"Ali Diba",
"Vivek Sharma",
"Reza Safdari",
"Dariush Lotfi",
"Saquib Sarfraz",
"Rainer Stiefelhagen",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Diba_Vi2CLR_Video_and_Image_for_Visual_Contrastive_Learning_of_Representation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Diba_Vi2CLR_Video_and_Image_for_Visual_Contrastive_Learning_of_Representation_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Diba_2021_ICCV,
author = {Diba, Ali and Sharma, Vivek and Safdari, Reza and Lotfi, Dariush and Sarfraz, Saquib and Stiefelhagen, Rainer and Van Gool, Luc},
title = {Vi2CLR: Video and Image for Visual Contrastive Learning of Representation},
booktitle = {Proceedings of the IEEE/CVF Inte... | In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion. The proposed neural network architecture and objectives are designed to obtain two different Convolutional Neural Networks for solving visual recognition tasks ... | [
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92 | R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth Estimating | [
"Zhongkai Zhou",
"Xinnan Fan",
"Pengfei Shi",
"Yuanxue Xin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_R-MSFM_Recurrent_Multi-Scale_Feature_Modulation_for_Monocular_Depth_Estimating_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_R-MSFM_Recurrent_Multi-Scale_Feature_Modulation_for_Monocular_Depth_Estimating_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Zhongkai and Fan, Xinnan and Shi, Pengfei and Xin, Yuanxue},
title = {R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth Estimating},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | In this paper, we propose Recurrent Multi-Scale Feature Modulation (R-MSFM), a new deep network architecture for self-supervised monocular depth estimation. R-MSFM extracts per-pixel features, builds a multi-scale feature modulation module, and iteratively updates an inverse depth through a parameter-shared decoder at ... | [
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93 | Spatially Conditioned Graphs for Detecting Human-Object Interactions | [
"Frederic Z. Zhang",
"Dylan Campbell",
"Stephen Gould"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Spatially_Conditioned_Graphs_for_Detecting_Human-Object_Interactions_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Spatially_Conditioned_Graphs_for_Detecting_Human-Object_Interactions_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhang_Spatially_Conditioned_Graphs_ICCV_2021_supplemental.pdf | 2012.06060 | cvf | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Frederic Z. and Campbell, Dylan and Gould, Stephen},
title = {Spatially Conditioned Graphs for Detecting Human-Object Interactions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October}... | We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in... | [
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94 | G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-Guided Feature Imitation | [
"Lewei Yao",
"Renjie Pi",
"Hang Xu",
"Wei Zhang",
"Zhenguo Li",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yao_G-DetKD_Towards_General_Distillation_Framework_for_Object_Detectors_via_Contrastive_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yao_G-DetKD_Towards_General_Distillation_Framework_for_Object_Detectors_via_Contrastive_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yao_G-DetKD_Towards_General_ICCV_2021_supplemental.pdf | 2108.07482 | title_snapshot | @InProceedings{Yao_2021_ICCV,
author = {Yao, Lewei and Pi, Renjie and Xu, Hang and Zhang, Wei and Li, Zhenguo and Zhang, Tong},
title = {G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-Guided Feature Imitation},
booktitle = {Proceedings of the IEEE/CV... | In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs. The conventional feature imitation paradigm introduces imitation masks to focus on informative foreground areas while exc... | [
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95 | End-to-End Detection and Pose Estimation of Two Interacting Hands | [
"Dong Uk Kim",
"Kwang In Kim",
"Seungryul Baek"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_End-to-End_Detection_and_Pose_Estimation_of_Two_Interacting_Hands_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_End-to-End_Detection_and_Pose_Estimation_of_Two_Interacting_Hands_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_End-to-End_Detection_and_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Kim_2021_ICCV,
author = {Kim, Dong Uk and Kim, Kwang In and Baek, Seungryul},
title = {End-to-End Detection and Pose Estimation of Two Interacting Hands},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Three dimensional hand pose estimation has reached a level of maturity, enabling real-world applications for single-hand cases. However, accurate estimation of the pose of two closely interacting hands still remains a challenge as in this case, one hand often occludes the other. We present a new algorithm that accurate... | [
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96 | Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather | [
"Martin Hahner",
"Christos Sakaridis",
"Dengxin Dai",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hahner_Fog_Simulation_on_Real_LiDAR_Point_Clouds_for_3D_Object_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hahner_Fog_Simulation_on_Real_LiDAR_Point_Clouds_for_3D_Object_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hahner_Fog_Simulation_on_ICCV_2021_supplemental.pdf | 2108.05249 | cvf | @InProceedings{Hahner_2021_ICCV,
author = {Hahner, Martin and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc},
title = {Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (... | This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing rea... | [
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97 | Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better | [
"Bojia Zi",
"Shihao Zhao",
"Xingjun Ma",
"Yu-Gang Jiang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zi_Revisiting_Adversarial_Robustness_Distillation_Robust_Soft_Labels_Make_Student_Better_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zi_Revisiting_Adversarial_Robustness_Distillation_Robust_Soft_Labels_Make_Student_Better_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zi_Revisiting_Adversarial_Robustness_ICCV_2021_supplemental.pdf | 2108.07969 | cvf | @InProceedings{Zi_2021_ICCV,
author = {Zi, Bojia and Zhao, Shihao and Ma, Xingjun and Jiang, Yu-Gang},
title = {Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mo... | Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their eff... | [
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98 | Normalization Matters in Weakly Supervised Object Localization | [
"Jeesoo Kim",
"Junsuk Choe",
"Sangdoo Yun",
"Nojun Kwak"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Normalization_Matters_in_Weakly_Supervised_Object_Localization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Normalization_Matters_in_Weakly_Supervised_Object_Localization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_Normalization_Matters_in_ICCV_2021_supplemental.pdf | 2107.13221 | cvf | @InProceedings{Kim_2021_ICCV,
author = {Kim, Jeesoo and Choe, Junsuk and Yun, Sangdoo and Kwak, Nojun},
title = {Normalization Matters in Weakly Supervised Object Localization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information. By simply training a classification model using only image-level annotations, the feature map of a model can be utilized as a score map for localization. In spite of many WSOL methods proposing n... | [
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99 | Joint Inductive and Transductive Learning for Video Object Segmentation | [
"Yunyao Mao",
"Ning Wang",
"Wengang Zhou",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Mao_Joint_Inductive_and_Transductive_Learning_for_Video_Object_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Mao_Joint_Inductive_and_Transductive_Learning_for_Video_Object_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Mao_Joint_Inductive_and_ICCV_2021_supplemental.pdf | 2108.03679 | cvf | @InProceedings{Mao_2021_ICCV,
author = {Mao, Yunyao and Wang, Ning and Zhou, Wengang and Li, Houqiang},
title = {Joint Inductive and Transductive Learning for Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oct... | Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information available makes it an extremely challenging task. Most previous best-performing methods adopt matching-based transductive reasoning or online i... | [
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