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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploit generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them that probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework, namely JoT-GAN, to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: (1) the adversarial training encourages it to fool the discriminator, and (2) the generated samples augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separate training and achieves state-of-the-art re-id performance.<\/jats:p>","DOI":"10.1145\/3491225","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T15:06:00Z","timestamp":1643123160000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["JoT-GAN: A Framework for Jointly Training GAN and Person Re-Identification Model"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1891-3276","authenticated-orcid":false,"given":"Zhongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1344-4415","authenticated-orcid":false,"given":"Ran","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1057-7909","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-7592","authenticated-orcid":false,"given":"Peng","family":"Duan","sequence":"additional","affiliation":[{"name":"Liaocheng University, Liaocheng, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4185-0127","authenticated-orcid":false,"given":"Youmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Qilu University of Technology, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00225"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.145"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2666805"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295397"},{"key":"e_1_3_1_6_2","first-page":"994","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Deng Weijian","year":"2018","unstructured":"Weijian Deng, Liang Zheng, Guoliang Kang, Yi Yang, Qixiang Ye, and Jianbin Jiao. 2018. 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