{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:12Z","timestamp":1750220172079,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548326","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:12Z","timestamp":1665416592000},"page":"3877-3885","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["DTR: An Information Bottleneck Based Regularization Framework for Video Action Recognition"],"prefix":"10.1145","author":[{"given":"Jiawei","family":"Fan","sequence":"first","affiliation":[{"name":"MEGVII Technology & Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"MEGVII Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xie","family":"Yu","sequence":"additional","affiliation":[{"name":"MEGVII Technology & Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Ma","sequence":"additional","affiliation":[{"name":"MEGVII Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqi","family":"Liu","sequence":"additional","affiliation":[{"name":"MEGVII Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangqiu","family":"Yi","sequence":"additional","affiliation":[{"name":"MEGVII Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boxun","family":"Li","sequence":"additional","affiliation":[{"name":"MEGVII Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2018.8503224"},{"key":"e_1_3_2_2_3_1","volume-title":"Learning robust representations via multi-view information bottleneck. arXiv preprint arXiv:2002.07017","author":"Federici Marco","year":"2020","unstructured":"Marco Federici , Anjan Dutta , Patrick Forr\u00e9 , Nate Kushman , and Zeynep Akata . Learning robust representations via multi-view information bottleneck. arXiv preprint arXiv:2002.07017 , 2020 . Marco Federici, Anjan Dutta, Patrick Forr\u00e9, Nate Kushman, and Zeynep Akata. Learning robust representations via multi-view information bottleneck. arXiv preprint arXiv:2002.07017, 2020."},{"key":"e_1_3_2_2_4_1","volume-title":"Shake-shake regularization. arXiv preprint arXiv:1705.07485","author":"Gastaldi Xavier","year":"2017","unstructured":"Xavier Gastaldi . Shake-shake regularization. arXiv preprint arXiv:1705.07485 , 2017 . Xavier Gastaldi. Shake-shake regularization. arXiv preprint arXiv:1705.07485, 2017."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.622"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"e_1_3_2_2_7_1","volume-title":"Aly El Gamal, and Hesham El Gamal. On the multi-view information bottleneck representation. arXiv preprint arXiv:2202.02684","author":"Huang Teng-Hui","year":"2022","unstructured":"Teng-Hui Huang , Aly El Gamal, and Hesham El Gamal. On the multi-view information bottleneck representation. arXiv preprint arXiv:2202.02684 , 2022 . Teng-Hui Huang, Aly El Gamal, and Hesham El Gamal. On the multi-view information bottleneck representation. arXiv preprint arXiv:2202.02684, 2022."},{"key":"e_1_3_2_2_8_1","volume-title":"arXiv preprint arXiv:2110.06178","author":"Huang Ziyuan","year":"2021","unstructured":"Ziyuan Huang , Shiwei Zhang , Liang Pan , Zhiwu Qing , Mingqian Tang , Ziwei Liu , and Marcelo H Ang Jr . Tada! temporally-adaptive convolutions for video understanding. arXiv preprint arXiv:2110.06178 , 2021 . Ziyuan Huang, Shiwei Zhang, Liang Pan, Zhiwu Qing, Mingqian Tang, Ziwei Liu, and Marcelo H Ang Jr. Tada! temporally-adaptive convolutions for video understanding. arXiv preprint arXiv:2110.06178, 2021."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00209"},{"key":"e_1_3_2_2_10_1","volume-title":"The kinetics human action video dataset. arXiv preprint arXiv:1705.06950","author":"Kay Will","year":"2017","unstructured":"Will Kay , Joao Carreira , Karen Simonyan , Brian Zhang , Chloe Hillier , Sudheendra Vijayanarasimhan , Fabio Viola , Tim Green , Trevor Back , Paul Natsev , The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 , 2017 . Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01212"},{"key":"e_1_3_2_2_12_1","first-page":"34","article-title":"What's missing in attention for video understanding","author":"Kim Manjin","year":"2021","unstructured":"Manjin Kim , Heeseung Kwon , Chunyu Wang , Suha Kwak , and Minsu Cho . Relational self-attention : What's missing in attention for video understanding . Advances in Neural Information Processing Systems , 34 , 2021 . Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, and Minsu Cho. Relational self-attention: What's missing in attention for video understanding. Advances in Neural Information Processing Systems, 34, 2021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00099"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_32"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00718"},{"key":"e_1_3_2_2_17_1","volume-title":"Stochastic gradient descent as approximate bayesian inference. arXiv preprint arXiv:1704.04289","author":"Mandt Stephan","year":"2017","unstructured":"Stephan Mandt , Matthew D Hoffman , and David M Blei . Stochastic gradient descent as approximate bayesian inference. arXiv preprint arXiv:1704.04289 , 2017 . Stephan Mandt, Matthew D Hoffman, and David M Blei. Stochastic gradient descent as approximate bayesian inference. arXiv preprint arXiv:1704.04289, 2017."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00269"},{"key":"e_1_3_2_2_19_1","volume-title":"Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810","author":"Shwartz-Ziv Ravid","year":"2017","unstructured":"Ravid Shwartz-Ziv and Naftali Tishby . Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810 , 2017 . Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017."},{"key":"e_1_3_2_2_20_1","volume-title":"Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402","author":"Soomro Khurram","year":"2012","unstructured":"Khurram Soomro , Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 , 2012 . Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 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_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00279"},{"key":"e_1_3_2_2_22_1","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"33","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian , Chen Sun , Ben Poole , Dilip Krishnan , Cordelia Schmid , and Phillip Isola . What makes for good views for contrastive learning? Advances in Neural Information Processing Systems , 33 : 6827 -- 6839 , 2020 . Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? Advances in Neural Information Processing Systems, 33:6827--6839, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_23_1","volume-title":"The information bottleneck method. arXiv preprint physics\/0004057","author":"Tishby Naftali","year":"2000","unstructured":"Naftali Tishby , Fernando C Pereira , and William Bialek . The information bottleneck method. arXiv preprint physics\/0004057 , 2000 . Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics\/0004057, 2000."},{"key":"e_1_3_2_2_24_1","first-page":"1","volume-title":"Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw)","author":"Tishby Naftali","year":"2015","unstructured":"Naftali Tishby and Noga Zaslavsky . Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw) , pages 1 -- 5 . IEEE , 2015 . Naftali Tishby and Noga Zaslavsky. Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw), pages 1--5. IEEE, 2015."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_2_26_1","volume-title":"Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038","author":"Tran Du","year":"2017","unstructured":"Du Tran , Jamie Ray , Zheng Shou , Shih-Fu Chang , and Manohar Paluri . Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038 , 2017 . Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, and Manohar Paluri. Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038, 2017."},{"key":"e_1_3_2_2_27_1","first-page":"6438","volume-title":"International Conference on Machine Learning","author":"Verma Vikas","year":"2019","unstructured":"Vikas Verma , Alex Lamb , Christopher Beckham , Amir Najafi , Ioannis Mitliagkas , David Lopez-Paz , and Yoshua Bengio . Manifold mixup : Better representations by interpolating hidden states . In International Conference on Machine Learning , pages 6438 -- 6447 . PMLR, 2019 . Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio. Manifold mixup: Better representations by interpolating hidden states. In International Conference on Machine Learning, pages 6438--6447. PMLR, 2019."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17210"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01163"},{"key":"e_1_3_2_2_30_1","first-page":"1895","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Tong Zhan","year":"2021","unstructured":"LiminWang, Zhan Tong , Bin Ji , and Gangshan Wu. Tdn : Temporal difference networks for efficient action recognition . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition , pages 1895 -- 1904 , 2021 . LiminWang, Zhan Tong, Bin Ji, and GangshanWu. Tdn: Temporal difference networks for efficient action recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 1895--1904, 2021."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"e_1_3_2_2_32_1","first-page":"153","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Girshick Ross","year":"2020","unstructured":"Chao-YuanWu, Ross Girshick , Kaiming He , Christoph Feichtenhofer , and Philipp Krahenbuhl . A multigrid method for efficiently training video models . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition , pages 153 -- 162 , 2020 . Chao-YuanWu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, and Philipp Krahenbuhl. A multigrid method for efficiently training video models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 153--162, 2020."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851993"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.11.054"},{"key":"e_1_3_2_2_35_1","first-page":"20437","article-title":"Graph information bottleneck","volume":"33","author":"Ren Hongyu","year":"2020","unstructured":"TailinWu, Hongyu Ren , Pan Li , and Jure Leskovec . Graph information bottleneck . Advances in Neural Information Processing Systems , 33 : 20437 -- 20448 , 2020 . TailinWu, Hongyu Ren, Pan Li, and Jure Leskovec. Graph information bottleneck. Advances in Neural Information Processing Systems, 33:20437--20448, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"e_1_3_2_2_37_1","volume-title":"Byeongho Heo, Dongyoon Han, and Jinhyung Kim. Videomix: Rethinking data augmentation for video classification. arXiv preprint arXiv:2012.03457","author":"Yun Sangdoo","year":"2020","unstructured":"Sangdoo Yun , Seong Joon Oh , Byeongho Heo, Dongyoon Han, and Jinhyung Kim. Videomix: Rethinking data augmentation for video classification. arXiv preprint arXiv:2012.03457 , 2020 . Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, and Jinhyung Kim. Videomix: Rethinking data augmentation for video classification. arXiv preprint arXiv:2012.03457, 2020."},{"key":"e_1_3_2_2_38_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang , Moustapha Cisse , Yann N Dauphin , and David Lopez-Paz . mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 , 2017 . Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017."},{"key":"e_1_3_2_2_39_1","volume-title":"The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects. arXiv preprint arXiv:1803.00195","author":"Zhu Zhanxing","year":"2018","unstructured":"Zhanxing Zhu , Jingfeng Wu , Bing Yu , Lei Wu , and Jinwen Ma . The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects. arXiv preprint arXiv:1803.00195 , 2018 . Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, and Jinwen Ma. The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects. arXiv preprint arXiv:1803.00195, 2018."}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Lisboa Portugal","acronym":"MM '22"},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548326","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:43Z","timestamp":1750186843000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":39,"alternative-id":["10.1145\/3503161.3548326","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548326","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}