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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>Unsupervised object Re-ID aims to learn discriminative identity features from a fully unlabeled dataset to solve the open-class re-identification problem. Satisfying results have been achieved in existing unsupervised Re-ID methods, primarily trained with pseudo-labels created by feature clustering. However, the viewpoint variation of objects is the key challenge, introducing noisy labels in the clustering process. To address this problem, a novel viewpoint disentangling and generation framework (VDG) is proposed to learn viewpoint-invariant ID features, including a disentangling and generation module, as well as a contrastive learning module. First, we design an ID encoder to map the viewpoint and identity features into the latent space. Second, a generator is used to disentangle view features and synthesize images with different orientations. Especially, the well-trained encoder serves as a pre-trained feature extractor in the contrastive learning module. Third, a viewpoint-aware loss and a class-level loss are integrated to facilitate contrastive learning between original and novel views. The generation of novel view images and the application of viewpoint-aware contrastive loss mutually assist model learning viewpoint-invariant ID features. Extensive experiments on Market-1501, DukeMTMC, MSMT17, and VeRi-776 demonstrate the effectiveness of the proposed VDG framework, as well as its superiority over the existing state-of-the-art approaches. The VDG model also demonstrates high quality in the image generation tasks.<\/jats:p>","DOI":"10.1145\/3632959","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T11:36:07Z","timestamp":1699961767000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Viewpoint Disentangling and Generation for Unsupervised Object Re-ID"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4459-4977","authenticated-orcid":false,"given":"Zongyi","family":"Li","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-5369","authenticated-orcid":false,"given":"Yuxuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-7412","authenticated-orcid":false,"given":"Hefei","family":"Ling","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2159-9393","authenticated-orcid":false,"given":"Jiazhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4163-6557","authenticated-orcid":false,"given":"Boyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7078-681X","authenticated-orcid":false,"given":"Runsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8269-9799","authenticated-orcid":false,"given":"Chengxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00453"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00204"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3226866"},{"key":"e_1_3_1_5_2","first-page":"232","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Chen Yanbei","year":"2019","unstructured":"Yanbei Chen, Xiatian Zhu, and Shaogang Gong. 2019. 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