{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:49:03Z","timestamp":1778082543735,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Google India PhD Fellowship"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,8]]},"DOI":"10.1145\/3571600.3571621","type":"proceedings-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T22:17:26Z","timestamp":1683929846000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5633-1843","authenticated-orcid":false,"given":"Avijit","family":"Dasgupta","sequence":"first","affiliation":[{"name":"CVIT, IIIT Hyderabad, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-7057","authenticated-orcid":false,"given":"C.V.","family":"Jawahar","sequence":"additional","affiliation":[{"name":"IIIT-Hyderabad, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1838-5936","authenticated-orcid":false,"given":"Karteek","family":"Alahari","sequence":"additional","affiliation":[{"name":"Inria, FR"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Devansh Arpit Stanislaw Jastrzebski Nicolas Ballas David Krueger Emmanuel Bengio Maxinder\u00a0S Kanwal Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio 2017. A closer look at memorization in deep networks. In ICML."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Joao Carreira and Andrew Zisserman. 2017. Quo vadis action recognition? a new model and the kinetics dataset. In CVPR.","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Min-Hung Chen Zsolt Kira Ghassan AlRegib Jaekwon Yoo Ruxin Chen and Jian Zheng. 2019. Temporal attentive alignment for large-scale video domain adaptation. In ICCV.","DOI":"10.1109\/ICCV.2019.00642"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Jinwoo Choi Gaurav Sharma Manmohan Chandraker and Jia-Bin Huang. 2020. Unsupervised and semi-supervised domain adaptation for action recognition from drones. In WACV.","DOI":"10.1109\/WACV45572.2020.9093511"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Jinwoo Choi Gaurav Sharma Samuel Schulter and Jia-Bin Huang. 2020. Shuffle and attend: Video domain adaptation. In ECCV.","DOI":"10.1007\/978-3-030-58610-2_40"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Victor G\u00a0Turrisi da Costa Giacomo Zara Paolo Rota Thiago Oliveira-Santos Nicu Sebe Vittorio Murino and Elisa Ricci. 2022. Dual-Head Contrastive Domain Adaptation for Video Action Recognition. In WACV.","DOI":"10.1109\/WACV51458.2022.00229"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Dima Damen Hazel Doughty Giovanni\u00a0Maria Farinella Sanja Fidler Antonino Furnari Evangelos Kazakos Davide Moltisanti Jonathan Munro Toby Perrett Will Price and Michael Wray. 2018. Scaling Egocentric Vision: The EPIC-KITCHENS Dataset. In ECCV.","DOI":"10.1007\/978-3-030-01225-0_44"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Christoph Feichtenhofer Haoqi Fan Jitendra Malik and Kaiming He. 2019. SlowFast Networks for Video Recognition. In ICCV.","DOI":"10.1109\/ICCV.2019.00630"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Christoph Feichtenhofer Axel Pinz and Andrew Zisserman. 2016. Convolutional two-stream network fusion for video action recognition. In CVPR.","DOI":"10.1109\/CVPR.2016.213"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Lei Feng Senlin Shu Zhuoyi Lin Fengmao Lv Li Li and Bo An. 2021. Can cross entropy loss be robust to label noise?. In IJCAI.","DOI":"10.24963\/ijcai.2020\/305"},{"key":"e_1_3_2_2_11_1","unstructured":"Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In ICML."},{"key":"e_1_3_2_2_12_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS","author":"Han Bo","year":"2018","unstructured":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS (2018)."},{"key":"e_1_3_2_2_13_1","volume-title":"Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. NeurIPS","author":"Huang Jiaxing","year":"2021","unstructured":"Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. 2021. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. NeurIPS (2021)."},{"key":"e_1_3_2_2_14_1","unstructured":"Arshad Jamal Vinay\u00a0P Namboodiri Dipti Deodhare and KS Venkatesh. 2018. Deep Domain Adaptation in Action Space.. In BMVC."},{"key":"e_1_3_2_2_15_1","unstructured":"Will Kay Joao Carreira Karen Simonyan Brian Zhang Chloe Hillier Sudheendra Vijayanarasimhan Fabio Viola Tim Green Trevor Back Paul Natsev Mustafa Suleyman and Andrew Zisserman. 2017. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950(2017)."},{"key":"e_1_3_2_2_16_1","volume-title":"Learning Cross-modal Contrastive Features for Video Domain Adaptation. ICCV","author":"Kim Donghyun","year":"2021","unstructured":"Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang, Xiang Yu, Stan Sclaroff, Kate Saenko, and Manmohan Chandraker. 2021. Learning Cross-modal Contrastive Features for Video Domain Adaptation. ICCV (2021)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3110179"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Hildegard Kuehne Hueihan Jhuang Est\u00edbaliz Garrote Tomaso Poggio and Thomas Serre. 2011. HMDB: a large video database for human motion recognition. In ICCV.","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Jogendra\u00a0Nath Kundu Naveen Venkat R\u00a0Venkatesh Babu 2020. Universal source-free domain adaptation. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00460"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Rui Li Qianfen Jiao Wenming Cao Hau-San Wong and Si Wu. 2020. Model adaptation: Unsupervised domain adaptation without source data. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"e_1_3_2_2_21_1","volume-title":"Adaptive batch normalization for practical domain adaptation. Pattern Recognition","author":"Li Yanghao","year":"2018","unstructured":"Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu. 2018. Adaptive batch normalization for practical domain adaptation. Pattern Recognition (2018)."},{"key":"e_1_3_2_2_22_1","unstructured":"Yunsheng Li Lu Yuan and Nuno Vasconcelos. 2019. Bidirectional learning for domain adaptation of semantic segmentation. In CVPR."},{"key":"e_1_3_2_2_23_1","unstructured":"J. Liang 2020. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In ICML."},{"key":"e_1_3_2_2_24_1","unstructured":"Mingsheng Long Yue Cao Jianmin Wang and Michael Jordan. 2015. Learning transferable features with deep adaptation networks. In ICML."},{"key":"e_1_3_2_2_25_1","unstructured":"Xingjun Ma Hanxun Huang Yisen Wang Simone Romano Sarah Erfani and James Bailey. 2020. Normalized loss functions for deep learning with noisy labels. In ICML."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Pietro Morerio Riccardo Volpi Ruggero Ragonesi and Vittorio Murino. 2020. Generative pseudo-label refinement for unsupervised domain adaptation. In WACV.","DOI":"10.1109\/WACV45572.2020.9093579"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Jonathan Munro and Dima Damen. 2020. Multi-modal domain adaptation for fine-grained action recognition. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00020"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Daniel Neimark Omri Bar Maya Zohar and Dotan Asselmann. 2021. Video transformer network. In ICCV.","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"e_1_3_2_2_29_1","unstructured":"Boxiao Pan Zhangjie Cao Ehsan Adeli and Juan\u00a0Carlos Niebles. 2020. Adversarial cross-domain action recognition with co-attention. In AAAI."},{"key":"e_1_3_2_2_30_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. NeurIPS","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. NeurIPS (2019)."},{"key":"e_1_3_2_2_31_1","volume-title":"Source-free domain adaptation via avatar prototype generation and adaptation. IJCAI","author":"Qiu Zhen","year":"2021","unstructured":"Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan. 2021. Source-free domain adaptation via avatar prototype generation and adaptation. IJCAI (2021)."},{"key":"e_1_3_2_2_32_1","volume-title":"Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing. NeurIPS","author":"Sahoo Aadarsh","year":"2021","unstructured":"Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, and Abir Das. 2021. Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing. NeurIPS (2021)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Kuniaki Saito Kohei Watanabe Yoshitaka Ushiku and Tatsuya Harada. 2018. Maximum classifier discrepancy for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2018.00392"},{"key":"e_1_3_2_2_34_1","volume-title":"Two-stream convolutional networks for action recognition in videos. NeurIPS","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. NeurIPS (2014)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Xiaolin Song Sicheng Zhao Jingyu Yang Huanjing Yue Pengfei Xu Runbo Hu and Hua Chai. 2021. Spatio-temporal Contrastive Domain Adaptation for Action Recognition. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00966"},{"key":"e_1_3_2_2_36_1","unstructured":"Khurram Soomro Amir\u00a0Roshan Zamir and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402(2012)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Antonio Torralba and Alexei\u00a0A Efros. 2011. Unbiased look at dataset bias. In CVPR.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Du Tran Lubomir Bourdev Rob Fergus Lorenzo Torresani and Manohar Paluri. 2015. Learning spatiotemporal features with 3d convolutional networks. In ICCV.","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Haoran Wang Tong Shen Wei Zhang Ling-Yu Duan and Tao Mei. 2020. Classes matter: A fine-grained adversarial approach to cross-domain semantic segmentation. In ECCV.","DOI":"10.1007\/978-3-030-58568-6_38"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Limin Wang Yuanjun Xiong Zhe Wang Yu Qiao Dahua Lin Xiaoou Tang and Luc\u00a0Van Gool. 2016. Temporal segment networks: Towards good practices for deep action recognition. In ECCV.","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Xiaolong Wang Ross Girshick Abhinav Gupta and Kaiming He. 2018. Non-local neural networks. In CVPR.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Yisen Wang Xingjun Ma Zaiyi Chen Yuan Luo Jinfeng Yi and James Bailey. 2019. Symmetric cross entropy for robust learning with noisy labels. In ICCV.","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_2_43_1","unstructured":"Hongxin Wei Lei Feng Xiangyu Chen and Bo An. 2020. Combating noisy labels by agreement: A joint training method with co-regularization. In CVPR."},{"key":"e_1_3_2_2_44_1","unstructured":"Chao-Yuan Wu Christoph Feichtenhofer Haoqi Fan Kaiming He Philipp Krahenbuhl and Ross Girshick. 2019. Long-Term Feature Banks for Detailed Video Understanding. In CVPR."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Jihan Yang Shaoshuai Shi Zhe Wang Hongsheng Li and Xiaojuan Qi. 2021. ST3D: Self-training for unsupervised domain adaptation on 3d object detection. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01023"},{"key":"e_1_3_2_2_46_1","volume-title":"Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation. NeurIPS","author":"S. Yang","year":"2021","unstructured":"S. Yang 2021. Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation. NeurIPS (2021)."},{"key":"e_1_3_2_2_47_1","volume-title":"Luis Herranz, and Shangling Jui.","author":"Yang Shiqi","year":"2020","unstructured":"Shiqi Yang, Yaxing Wang, Joost van\u00a0de Weijer, Luis Herranz, and Shangling Jui. 2020. Unsupervised domain adaptation without source data by casting a bait. arXiv preprint arXiv:2010.12427(2020)."},{"key":"e_1_3_2_2_48_1","unstructured":"Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor Tsang and Masashi Sugiyama. 2019. How does disagreement help generalization against label corruption?. In ICML."},{"key":"e_1_3_2_2_49_1","unstructured":"Christopher Zach Thomas Pock and Horst Bischof. 2007. A duality based approach for realtime tv-l 1 optical flow. In Joint pattern recognition symposium."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Pan Zhang Bo Zhang Ting Zhang Dong Chen Yong Wang and Fang Wen. 2021. Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"e_1_3_2_2_51_1","volume-title":"Temporal Relational Reasoning in Videos. ECCV","author":"Zhou Bolei","year":"2018","unstructured":"Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. 2018. Temporal Relational Reasoning in Videos. ECCV (2018)."}],"event":{"name":"ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing","location":"Gandhinagar India","acronym":"ICVGIP'22"},"container-title":["Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571600.3571621","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3571600.3571621","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:09Z","timestamp":1750183749000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571600.3571621"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":51,"alternative-id":["10.1145\/3571600.3571621","10.1145\/3571600"],"URL":"https:\/\/doi.org\/10.1145\/3571600.3571621","relation":{},"subject":[],"published":{"date-parts":[[2022,12,8]]},"assertion":[{"value":"2023-05-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}