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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person\u2019s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1145\/3542820","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T13:13:51Z","timestamp":1654780431000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0179-1396","authenticated-orcid":false,"given":"Yue","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Information Science and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-7131","authenticated-orcid":false,"given":"Fanghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8408-3816","authenticated-orcid":false,"given":"Yi","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6255-9422","authenticated-orcid":false,"given":"Yigang","family":"Cen","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8114-6383","authenticated-orcid":false,"given":"Viacheslav","family":"Voronin","sequence":"additional","affiliation":[{"name":"Center for Cognitive Technology and Machine Vision, Moscow StateUniversity of Technology \u201cSTANKIN\u201d, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-008-9066-8"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV\u201919) Workshops","author":"Cao Yue","year":"2019","unstructured":"Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han Hu. 2019. 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