{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:29:18Z","timestamp":1760956158430,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":55,"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"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62132002; 61922006"],"award-info":[{"award-number":["62132002; 61922006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547812","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:35Z","timestamp":1665416555000},"page":"1758-1768","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Revisiting Stochastic Learning for Generalizable Person Re-identification"],"prefix":"10.1145","author":[{"given":"Jiajian","family":"Zhao","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowu","family":"Chen","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, 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":"10.1609\/aaai.v35i2.16190"},{"key":"e_1_3_2_2_2_1","first-page":"2039","article-title":"Just pick a sign: Optimizing deep multitask models with gradient sign dropout","volume":"33","author":"Chen Zhao","year":"2020","unstructured":"Zhao Chen , Jiquan Ngiam , Yanping Huang , Thang Luong , Henrik Kretzschmar , Yuning Chai , and Dragomir Anguelov . 2020 . Just pick a sign: Optimizing deep multitask models with gradient sign dropout . Advances in Neural Information Processing Systems 33 (2020), 2039 -- 2050 . Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. 2020. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. Advances in Neural Information Processing Systems 33 (2020), 2039--2050.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00343"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01588"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00110"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01411"},{"volume-title":"Learning from imbalanced data sets","author":"Fern\u00e1ndez Alberto","key":"e_1_3_2_2_7_1","unstructured":"Alberto Fern\u00e1ndez , Salvador Garc\u00eda , Mikel Galar , Ronaldo C Prati , Bartosz Krawczyk , and Francisco Herrera . 2018. Learning from imbalanced data sets . Vol. 10 . Springer . Alberto Fern\u00e1ndez, Salvador Garc\u00eda, Mikel Galar, Ronaldo C Prati, Bartosz Krawczyk, and Francisco Herrera. 2018. Learning from imbalanced data sets. Vol. 10. Springer."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_21"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_3_2_2_10_1","volume-title":"Fastreid: A pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631","author":"He Lingxiao","year":"2020","unstructured":"Lingxiao He , Xingyu Liao , Wu Liu , Xinchen Liu , Peng Cheng , and Tao Mei . 2020 . Fastreid: A pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631 (2020). Lingxiao He, Xingyu Liao, Wu Liu, Xinchen Liu, Peng Cheng, and Tao Mei. 2020. Fastreid: A pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631 (2020)."},{"key":"e_1_3_2_2_11_1","unstructured":"Alexander Hermans Lucas Beyer and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017).  Alexander Hermans Lucas Beyer and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21227-7_9"},{"key":"e_1_3_2_2_13_1","volume-title":"Frustratingly easy person re-identification: Generalizing person re-id in practice. arXiv preprint arXiv:1905.03422","author":"Jia Jieru","year":"2019","unstructured":"Jieru Jia , Qiuqi Ruan , and Timothy M Hospedales . 2019. Frustratingly easy person re-identification: Generalizing person re-id in practice. arXiv preprint arXiv:1905.03422 ( 2019 ). Jieru Jia, Qiuqi Ruan, and Timothy M Hospedales. 2019. Frustratingly easy person re-identification: Generalizing person re-id in practice. arXiv preprint arXiv:1905.03422 (2019)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00321"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475455"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.461"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.27"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_38"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_27"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-010-0347-5"},{"key":"e_1_3_2_2_24_1","volume-title":"International Conference on Machine Learning. PMLR, 7313--7324","author":"Mahajan Divyat","year":"2021","unstructured":"Divyat Mahajan , Shruti Tople , and Amit Sharma . 2021 . Domain generalization using causal matching . In International Conference on Machine Learning. PMLR, 7313--7324 . Divyat Mahajan, Shruti Tople, and Amit Sharma. 2021. Domain generalization using causal matching. In International Conference on Machine Learning. PMLR, 7313--7324."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Machine Learning. PMLR, 10--18","author":"Muandet Krikamol","year":"2013","unstructured":"Krikamol Muandet , David Balduzzi , and Bernhard Sch\u00f6lkopf . 2013 . Domain generalization via invariant feature representation . In International Conference on Machine Learning. PMLR, 10--18 . Krikamol Muandet, David Balduzzi, and Bernhard Sch\u00f6lkopf. 2013. Domain generalization via invariant feature representation. In International Conference on Machine Learning. PMLR, 10--18."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00051"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00081"},{"key":"e_1_3_2_2_30_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava , Geoffrey Hinton , Alex Krizhevsky , Ilya Sutskever , and Ruslan Salakhutdinov . 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 ( 2014 ), 1929--1958. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929--1958."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_30"},{"key":"e_1_3_2_2_32_1","volume-title":"Andrew KC Wong, and Mohamed S Kamel","author":"Sun Yanmin","year":"2009","unstructured":"Yanmin Sun , Andrew KC Wong, and Mohamed S Kamel . 2009 . Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence 23, 04 (2009), 687--719. Yanmin Sun, Andrew KC Wong, and Mohamed S Kamel. 2009. Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence 23, 04 (2009), 687--719."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"e_1_3_2_2_34_1","volume-title":"Augmented hard example mining for generalizable person re-identification. arXiv preprint arXiv:1910.05280","author":"Tamura Masato","year":"2019","unstructured":"Masato Tamura and Tomokazu Murakami . 2019. Augmented hard example mining for generalizable person re-identification. arXiv preprint arXiv:1910.05280 ( 2019 ). Masato Tamura and Tomokazu Murakami. 2019. Augmented hard example mining for generalizable person re-identification. arXiv preprint arXiv:1910.05280 (2019)."},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of the Asian Conference on Computer Vision.","author":"Tseng Hung-Yu","year":"2020","unstructured":"Hung-Yu Tseng , Yi-Wen Chen , Yi-Hsuan Tsai , Sifei Liu , Yen-Yu Lin , and Ming-Hsuan Yang . 2020 . Regularizing meta-learning via gradient dropout . In Proceedings of the Asian Conference on Computer Vision. Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai, Sifei Liu, Yen-Yu Lin, and Ming-Hsuan Yang. 2020. Regularizing meta-learning via gradient dropout. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_2_2_36_1","volume-title":"Generalizing to unseen domains via adversarial data augmentation. Advances in neural information processing systems 31","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi , Hongseok Namkoong , Ozan Sener , John C Duchi , Vittorio Murino , and Silvio Savarese . 2018. Generalizing to unseen domains via adversarial data augmentation. Advances in neural information processing systems 31 ( 2018 ). Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413815"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00016"},{"volume-title":"Deep cosine metric learning for person re-identification. In 2018 IEEE winter conference on applications of computer vision (WACV)","author":"Wojke Nicolai","key":"e_1_3_2_2_39_1","unstructured":"Nicolai Wojke and Alex Bewley . 2018. Deep cosine metric learning for person re-identification. In 2018 IEEE winter conference on applications of computer vision (WACV) . IEEE , 748--756. Nicolai Wojke and Alex Bewley. 2018. Deep cosine metric learning for person re-identification. In 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 748--756."},{"key":"e_1_3_2_2_40_1","volume-title":"End-to-end deep learning for person search. arXiv preprint arXiv:1604.01850 2, 2","author":"Xiao Tong","year":"2016","unstructured":"Tong Xiao , Shuang Li , Bochao Wang , Liang Lin , and Xiaogang Wang . 2016. End-to-end deep learning for person search. arXiv preprint arXiv:1604.01850 2, 2 ( 2016 ), 4. Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. 2016. End-to-end deep learning for person search. arXiv preprint arXiv:1604.01850 2, 2 (2016), 4."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.360"},{"key":"e_1_3_2_2_42_1","volume-title":"Robust and generalizable visual representation learning via random convolutions. arXiv preprint arXiv:2007.13003","author":"Xu Zhenlin","year":"2020","unstructured":"Zhenlin Xu , Deyi Liu , Junlin Yang , Colin Raffel , and Marc Niethammer . 2020. Robust and generalizable visual representation learning via random convolutions. arXiv preprint arXiv:2007.13003 ( 2020 ). Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, and Marc Niethammer. 2020. Robust and generalizable visual representation learning via random convolutions. arXiv preprint arXiv:2007.13003 (2020)."},{"key":"e_1_3_2_2_43_1","volume-title":"Deep learning for person re-identification: A survey and outlook","author":"Ye Mang","year":"2021","unstructured":"Mang Ye , Jianbing Shen , Gaojie Lin , Tao Xiang , Ling Shao , and Steven CH Hoi . 2021. Deep learning for person re-identification: A survey and outlook . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2021 ). Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. 2021. Deep learning for person re-identification: A survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00831"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00621"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_52"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919877"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.357"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.405"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.405"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.389"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00069"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3069237"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"e_1_3_2_2_55_1","unstructured":"Kaiyang Zhou Yongxin Yang Yu Qiao and Tao Xiang. 2021. Domain Generalization with MixStyle. In ICLR.  Kaiyang Zhou Yongxin Yang Yu Qiao and Tao Xiang. 2021. Domain Generalization with MixStyle. In ICLR."}],"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\/3503161.3547812","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547812","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:34Z","timestamp":1750186954000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":55,"alternative-id":["10.1145\/3503161.3547812","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547812","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"}}]}}