{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:14:01Z","timestamp":1753884841185,"version":"3.41.2"},"reference-count":68,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"name":"National Natural Science Fund of Shanxi Province","award":["201901D111154"],"award-info":[{"award-number":["201901D111154"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p> Person Re-ID is challenged by background clutter, body misalignment and part missing. In this paper, we propose a reliable part-based multiple levels attention deep network to learn multiple scales salience representation. In particular, person alignment and key point detection are sequentially carried out to locate three relative stable body components, then fused attention (FA) mode is designed to capture the fine-grained salient features from effective spatial of valuable channels of each part, regional attention mode is succeeded to weight the importance of different parts for highlighting the representative parts while suppressing the valueless ones. A late fusion-based multiple-task loss is finally adopted to further optimize the valuable feature representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03. <\/jats:p>","DOI":"10.1142\/s0218126621502467","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T08:23:48Z","timestamp":1621931028000},"source":"Crossref","is-referenced-by-count":2,"title":["Reliable Part Guided Multiple Level Attention Learning for Person Re-Identification"],"prefix":"10.1142","volume":"30","author":[{"given":"Yanbing","family":"Geng","sequence":"first","affiliation":[{"name":"School of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjian","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunmin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-3641","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, 174 Shazheng Street, Shapingba District, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingchao","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"S0218126621502467BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107036"},{"key":"S0218126621502467BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.06.006"},{"key":"S0218126621502467BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927170"},{"key":"S0218126621502467BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.06.006"},{"key":"S0218126621502467BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2910414"},{"key":"S0218126621502467BIB006","first-page":"369","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Shuang L.","year":"2018"},{"key":"S0218126621502467BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2873599"},{"key":"S0218126621502467BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00747"},{"key":"S0218126621502467BIB009","doi-asserted-by":"publisher","DOI":"10.1142\/S021812662050190X"},{"key":"S0218126621502467BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.358"},{"key":"S0218126621502467BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.186"},{"key":"S0218126621502467BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2700762"},{"key":"S0218126621502467BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.46"},{"key":"S0218126621502467BIB015","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.144"},{"key":"S0218126621502467BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2910414"},{"key":"S0218126621502467BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2873599"},{"key":"S0218126621502467BIB018","first-page":"2150096","author":"Chen C.","year":"2020","journal-title":"J. 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