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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>\n            Person\n            <jats:bold>re-identification (re-id)<\/jats:bold>\n            attempts to match pedestrian images with the same identity across non-overlapping cameras. Existing methods usually study person re-id by learning discriminative features based on the clothing attributes (e.g., color, texture). However, the clothing appearance is not sufficient to distinguish different persons especially when they are in similar clothes, which is known as the\n            <jats:bold>fine-grained (FG)<\/jats:bold>\n            person re-id problem. By contrast, this paper proposes to exploit the color-unrelated feature along with the head-shoulder feature for FG person re-id. Specifically, a\n            <jats:bold>color-unrelated head-shoulder network (CUHS)<\/jats:bold>\n            is developed, which is featured in three aspects: (1) It consists of a lightweight head-shoulder segmentation layer for localizing the head-shoulder region and learning the corresponding feature. (2) It exploits\n            <jats:bold>instance normalization (IN)<\/jats:bold>\n            for learning color-unrelated features. (3) As IN inevitably reduces inter-class differences, we propose to explore richer visual cues for IN by an attention exploration mechanism to ensure high discrimination. We evaluate our model on the FG-reID, Market1501, and DukeMTMC-reID datasets, and the results show that CUHS surpasses previous methods on both the FG and conventional person re-id problems.\n          <\/jats:p>","DOI":"10.1145\/3599730","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T11:34:43Z","timestamp":1685014483000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Color-Unrelated Head-Shoulder Networks for Fine-Grained Person Re-identification"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1465-5553","authenticated-orcid":false,"given":"Boqiang","family":"Xu","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1894","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"additional","affiliation":[{"name":"CRIPAC &amp; MAIS, Institute of Automation, Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0861-8177","authenticated-orcid":false,"given":"Lingxiao","family":"He","sequence":"additional","affiliation":[{"name":"AI Research of JD, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7877-5728","authenticated-orcid":false,"given":"Jinlin","family":"Wu","sequence":"additional","affiliation":[{"name":"MAIS, Institute of Automation, Chinese Academy of Sciences; Centre for Artificial Intelligence and Robotics, HKISI, CAS, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5971-5352","authenticated-orcid":false,"given":"Chao","family":"Fan","sequence":"additional","affiliation":[{"name":"Chengdu Discaray Technology Co., Ltd., China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-9935","authenticated-orcid":false,"given":"Zhenan","family":"Sun","sequence":"additional","affiliation":[{"name":"CRIPAC &amp; MAIS, Institute of Automation, Chinese Academy of Sciences, China"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Deep-person: Learning discriminative deep features for person re-identification","volume":"98","author":"Bai Xiang","year":"2020","unstructured":"Xiang Bai, Mingkun Yang, Tengteng Huang, Zhiyong Dou, Rui Yu, and Yongchao Xu. 2020. 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