ICCV
Collection
Accepted papers for ICCV (IEEE/CVF International Conference on Computer Vision), one dataset per year. • 7 items • Updated
paper_id uint32 0 1.61k | title stringlengths 15 141 | authors listlengths 1 18 | cvf_url stringlengths 92 183 | pdf_url stringlengths 93 184 | supp_url stringlengths 101 180 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 304 677 | abstract large_stringlengths 562 2.21k | embedding listlengths 768 768 |
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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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