ICCV
Collection
Accepted papers for ICCV (IEEE/CVF International Conference on Computer Vision), one dataset per year. • 7 items • Updated
paper_id uint32 0 2.16k | title stringlengths 15 155 | authors listlengths 1 25 | cvf_url stringlengths 96 198 | pdf_url stringlengths 97 199 | supp_url stringlengths 100 189 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 304 793 | abstract large_stringlengths 524 2.16k | embedding listlengths 768 768 |
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0 | Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | [
"Sungwon Han",
"Sungwon Park",
"Fangzhao Wu",
"Sundong Kim",
"Bin Zhu",
"Xing Xie",
"Meeyoung Cha"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Han_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Han_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Han_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf | 2308.09318 | cvf | @InProceedings{Han_2023_ICCV,
author = {Han, Sungwon and Park, Sungwon and Wu, Fangzhao and Kim, Sundong and Zhu, Bin and Xie, Xing and Cha, Meeyoung},
title = {Towards Attack-tolerant Federated Learning via Critical Parameter Analysis},
booktitle = {Proceedings of the IEEE/CVF International Conferen... | Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. Existing defense strategies are ineffective under non-IID data ... | [
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1 | Stochastic Segmentation with Conditional Categorical Diffusion Models | [
"Lukas Zbinden",
"Lars Doorenbos",
"Theodoros Pissas",
"Adrian Thomas Huber",
"Raphael Sznitman",
"Pablo Márquez-Neila"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Zbinden_Stochastic_Segmentation_with_Conditional_Categorical_Diffusion_Models_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Zbinden_Stochastic_Segmentation_with_Conditional_Categorical_Diffusion_Models_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zbinden_Stochastic_Segmentation_with_ICCV_2023_supplemental.pdf | 2303.08888 | cvf | @InProceedings{Zbinden_2023_ICCV,
author = {Zbinden, Lukas and Doorenbos, Lars and Pissas, Theodoros and Huber, Adrian Thomas and Sznitman, Raphael and M\'arquez-Neila, Pablo},
title = {Stochastic Segmentation with Conditional Categorical Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF In... | Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving. Instead,... | [
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2 | Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model | [
"Xunpeng Yi",
"Han Xu",
"Hao Zhang",
"Linfeng Tang",
"Jiayi Ma"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Yi_Diff-Retinex_Rethinking_Low-light_Image_Enhancement_with_A_Generative_Diffusion_Model_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Yi_Diff-Retinex_Rethinking_Low-light_Image_Enhancement_with_A_Generative_Diffusion_Model_ICCV_2023_paper.pdf | null | 2308.13164 | title_snapshot | @InProceedings{Yi_2023_ICCV,
author = {Yi, Xunpeng and Xu, Han and Zhang, Hao and Tang, Linfeng and Ma, Jiayi},
title = {Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)... | In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even ded... | [
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3 | Bird's-Eye-View Scene Graph for Vision-Language Navigation | [
"Rui Liu",
"Xiaohan Wang",
"Wenguan Wang",
"Yi Yang"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Liu_Birds-Eye-View_Scene_Graph_for_Vision-Language_Navigation_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Liu_Birds-Eye-View_Scene_Graph_for_Vision-Language_Navigation_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Liu_Birds-Eye-View_Scene_Graph_ICCV_2023_supplemental.pdf | 2308.04758 | title_snapshot | @InProceedings{Liu_2023_ICCV,
author = {Liu, Rui and Wang, Xiaohan and Wang, Wenguan and Yang, Yi},
title = {Bird's-Eye-View Scene Graph for Vision-Language Navigation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Vision-language navigation (VLN), which entails an agent to navigate 3D environments following human instructions, has shown great advances. However, current agents are built upon panoramic observations, which hinders their ability to perceive 3D scene geometry and easily leads to ambiguous selection of panoramic view.... | [
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4 | PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework | [
"Bowen Li",
"Ziyuan Huang",
"Junjie Ye",
"Yiming Li",
"Sebastian Scherer",
"Hang Zhao",
"Changhong Fu"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Li_PVT_A_Simple_End-to-End_Latency-Aware_Visual_Tracking_Framework_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Li_PVT_A_Simple_End-to-End_Latency-Aware_Visual_Tracking_Framework_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Li_PVT_A_Simple_ICCV_2023_supplemental.pdf | 2211.11629 | title_snapshot | @InProceedings{Li_2023_ICCV,
author = {Li, Bowen and Huang, Ziyuan and Ye, Junjie and Li, Yiming and Scherer, Sebastian and Zhao, Hang and Fu, Changhong},
title = {PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework},
booktitle = {Proceedings of the IEEE/CVF International Conference on... | Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicles (UAVs), where robust tracking is more challenging and onboard computation is limited, th... | [
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5 | A Dynamic Dual-Processing Object Detection Framework Inspired by the Brain's Recognition Mechanism | [
"Minying Zhang",
"Tianpeng Bu",
"Lulu Hu"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_A_Dynamic_Dual-Processing_Object_Detection_Framework_Inspired_by_the_Brains_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_A_Dynamic_Dual-Processing_Object_Detection_Framework_Inspired_by_the_Brains_ICCV_2023_paper.pdf | null | null | null | @InProceedings{Zhang_2023_ICCV,
author = {Zhang, Minying and Bu, Tianpeng and Hu, Lulu},
title = {A Dynamic Dual-Processing Object Detection Framework Inspired by the Brain's Recognition Mechanism},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | There are two main approaches to object detection: CNN-based and Transformer-based. The former views object detection as a dense local matching problem, while the latter sees it as a sparse global retrieval problem. Research in neuroscience has shown that the recognition decision in the brain is based on two processes,... | [
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6 | Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient | [
"Zhengzhi Lu",
"He Wang",
"Ziyi Chang",
"Guoan Yang",
"Hubert P. H. Shum"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Lu_Hard_No-Box_Adversarial_Attack_on_Skeleton-Based_Human_Action_Recognition_with_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_Hard_No-Box_Adversarial_Attack_on_Skeleton-Based_Human_Action_Recognition_with_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Lu_Hard_No-Box_Adversarial_ICCV_2023_supplemental.zip | 2308.05681 | cvf | @InProceedings{Lu_2023_ICCV,
author = {Lu, Zhengzhi and Wang, He and Chang, Ziyi and Yang, Guoan and Shum, Hubert P. H.},
title = {Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient},
booktitle = {Proceedings of the IEEE/CVF International ... | Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (i.e. transfer-based attacks) or frequent model queries (i.e. black-box... | [
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7 | GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving | [
"Zhiyu Huang",
"Haochen Liu",
"Chen Lv"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Huang_GameFormer_Game-theoretic_Modeling_and_Learning_of_Transformer-based_Interactive_Prediction_and_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Huang_GameFormer_Game-theoretic_Modeling_and_Learning_of_Transformer-based_Interactive_Prediction_and_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Huang_GameFormer_Game-theoretic_Modeling_ICCV_2023_supplemental.pdf | 2303.05760 | cvf | @InProceedings{Huang_2023_ICCV,
author = {Huang, Zhiyu and Liu, Haochen and Lv, Chen},
title = {GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compu... | Autonomous vehicles operating in complex real-world environments require accurate predictions of interactive behaviors between traffic participants. This paper tackles the interaction prediction problem by formulating it with hierarchical game theory and proposing the GameFormer model for its implementation. The model ... | [
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8 | Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation | [
"Zhiqiang Gao",
"Kaizhu Huang",
"Rui Zhang",
"Dawei Liu",
"Jieming Ma"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Gao_Towards_Better_Robustness_against_Common_Corruptions_for_Unsupervised_Domain_Adaptation_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Gao_Towards_Better_Robustness_against_Common_Corruptions_for_Unsupervised_Domain_Adaptation_ICCV_2023_paper.pdf | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Gao_Towards_Better_Robustness_ICCV_2023_supplemental.pdf | null | null | @InProceedings{Gao_2023_ICCV,
author = {Gao, Zhiqiang and Huang, Kaizhu and Zhang, Rui and Liu, Dawei and Ma, Jieming},
title = {Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Recent studies have investigated how to achieve robustness for unsupervised domain adaptation (UDA). While most efforts focus on adversarial robustness, i.e. how the model performs against unseen malicious adversarial perturbations, robustness against benign common corruption (RaCC) surprisingly remains under-explored ... | [
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9 | Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels | [
"Wenqiao Zhang",
"Changshuo Liu",
"Lingze Zeng",
"Bengchin Ooi",
"Siliang Tang",
"Yueting Zhuang"
] | https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Learning_in_Imperfect_Environment_Multi-Label_Classification_with_Long-Tailed_Distribution_and_ICCV_2023_paper.html | https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Learning_in_Imperfect_Environment_Multi-Label_Classification_with_Long-Tailed_Distribution_and_ICCV_2023_paper.pdf | null | 2304.10539 | cvf | @InProceedings{Zhang_2023_ICCV,
author = {Zhang, Wenqiao and Liu, Changshuo and Zeng, Lingze and Ooi, Bengchin and Tang, Siliang and Zhuang, Yueting},
title = {Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels},
booktitle = {Proceedings of ... | Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data
that has long-tailed (LT) distribution and partial labels (PL).
To address the problem, we introduce a novel task, Partial... | [
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