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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Unsupervised person re-identification (Re-ID) has garnered significant attention because of its data-friendly nature, as it does not require labeled data. Existing approaches primarily address this challenge by employing feature-clustering techniques to generate pseudo-labels. In addition, camera-proxy-based methods have emerged because of their impressive ability to cluster sample identities. However, these methods often blur the distinctions between individuals within inter-camera views, which is crucial for effective person re-ID. To address this issue, this study introduces an inter-camera-identity-difference-based contrastive learning framework for unsupervised person Re-ID. The proposed framework comprises two key components: (1)\u00a0a different sample cross-view close-range penalty module and (2) the same sample cross-view long-range constraint module. The former aims at penalizing excessive similarity among different subjects across inter-camera views, whereas the latter mitigates the challenge of excessive dissimilarity among the same subject across camera views. To validate the performance of our method, we conducted extensive experiments on three existing person Re-ID datasets (Market-1501, MSMT17, and PersonX). The results demonstrate the effectiveness of the proposed method, which shows a promising performance. The code is available at\u00a0\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/hooldylan\/IIDCL\">https:\/\/github.com\/hooldylan\/IIDCL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3652858","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T12:10:11Z","timestamp":1712146211000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Inter-camera Identity Discrimination for Unsupervised Person Re-identification"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0487-0356","authenticated-orcid":false,"given":"Mingfu","family":"Xiong","sequence":"first","affiliation":[{"name":"Wuhan Textile University, Wuhan, China and Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9517-1956","authenticated-orcid":false,"given":"Kaikang","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan Textile University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7662-7473","authenticated-orcid":false,"given":"Zhihan","family":"Lyu","sequence":"additional","affiliation":[{"name":"Uppsala University, Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1792-519X","authenticated-orcid":false,"given":"Fei","family":"Fang","sequence":"additional","affiliation":[{"name":"Wuhan Textile University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3354-1877","authenticated-orcid":false,"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5481-287X","authenticated-orcid":false,"given":"Ruimin","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4055-7412","authenticated-orcid":false,"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.358"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01469"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00204"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44267-023-00029-4"},{"key":"e_1_3_1_6_2","first-page":"1597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. 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