{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:36:06Z","timestamp":1760236566087,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/a14120361","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T01:20:41Z","timestamp":1639444841000},"page":"361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer"],"prefix":"10.3390","volume":"14","author":[{"given":"Chengyan","family":"Zhong","sequence":"first","affiliation":[{"name":"Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-3865","authenticated-orcid":false,"given":"Guanqiu","family":"Qi","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neal","family":"Mazur","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarbani","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devanshi","family":"Malaviya","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Hu","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. 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