{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:46:29Z","timestamp":1777603589525,"version":"3.51.4"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"11","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62276221, No. 62376232, No. 62466003"],"award-info":[{"award-number":["No. 62276221, No. 62376232, No. 62466003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fujian Provincial Natural Science Foundation of China","award":["2022J01002"],"award-info":[{"award-number":["2022J01002"]}]},{"name":"Natural Science Foundation of Guangxi Province of China","award":["No. 2023JJB170012, No. 2024JJA170003"],"award-info":[{"award-number":["No. 2023JJB170012, No. 2024JJA170003"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["FKLBDAITI202304"],"award-info":[{"award-number":["FKLBDAITI202304"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match a person across two modalities without annotations. Current research primarily addresses the modality gap by establishing cross-modality correspondences through matching algorithms and utilizing memory banks for contrastive learning. However, the inherent noise in pseudo labels and neglect of hard samples often limit the efficacy of cross-modality learning. In this article, we propose a dual-modality-shared learning and label refinement (DLLR) algorithm for USVI-ReID. First, we leverage a cluster similarity matching (CSM) module and a cluster relationship-based label refinement (CRLR) algorithm to create and refine pseudo labels. Then, we adopt a weighted modality-shared memory (WMM) to construct memory banks by jointly considering sample distribution and feature differences, thereby enhancing the effectiveness of cross-modality learning. Extensive experiments on three publicly available datasets validate the effectiveness of our proposed method, which outperforms state-of-the-art methods. The code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/CharRic\/DLLR\">https:\/\/github.com\/CharRic\/DLLR<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3724397","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T10:31:20Z","timestamp":1742380280000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual-Modality-Shared Learning and Label Refinement for Unsupervised Visible-Infrared Person ReID"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4452-1336","authenticated-orcid":false,"given":"Licun","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Xiamen University, Xiamen,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-9582","authenticated-orcid":false,"given":"Zhiming","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Xiamen University, Xiamen,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1582-0987","authenticated-orcid":false,"given":"Yongguo","family":"Ling","sequence":"additional","affiliation":[{"name":"The School of Computer, Electronics and Information, Guangxi University, Nanning,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8653-8986","authenticated-orcid":false,"given":"Jiaxing","family":"Chai","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Xiamen University, Xiamen,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5403-9945","authenticated-orcid":false,"given":"Shaozi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Xiamen University, Xiamen, China and Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Wuyishan,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"14960","volume-title":"ICCV","author":"Chen Hao","year":"2021","unstructured":"Hao Chen, Benoit Lagadec, and Francois Bremond. 2021. 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