{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T04:16:05Z","timestamp":1784261765967,"version":"3.55.0"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225207"],"award-info":[{"award-number":["62225207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19B2038"],"award-info":[{"award-number":["U19B2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106245"],"award-info":[{"award-number":["62106245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tmm.2023.3333190","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T19:01:51Z","timestamp":1700074911000},"page":"5422-5434","source":"Crossref","is-referenced-by-count":5,"title":["Unleashing Knowledge Potential of Source Hypothesis for Source-Free Domain Adaptation"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6482-2803","authenticated-orcid":false,"given":"Bingyu","family":"Hu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9940-6366","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3450-400X","authenticated-orcid":false,"given":"Kecheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2510-8993","authenticated-orcid":false,"given":"Zheng-Jun","family":"Zha","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref2","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 3rd Int. Conf. Learn. Representations Comput. Biol. Learn. Soc.","author":"Simonyan","year":"2015"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00517"},{"key":"ref5","article-title":"Conditional adversarial domain adaptation","volume":"31","author":"Long","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref6","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long","year":"2015"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_37"},{"key":"ref8","article-title":"CDTRANS: Cross-domain transformer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref10","first-page":"6028","article-title":"Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/402"},{"key":"ref12","article-title":"Transformer-based source-free domain adaptation","volume":"abs\/2105.14138","author":"Yang","year":"2021","journal-title":"CoRR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00888"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103747"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ac0e74"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00707"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00460"},{"key":"ref19","first-page":"942","article-title":"Stability and hypothesis transfer learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kuzborskij","year":"2013"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20103"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01185"},{"key":"ref23","article-title":"On the adversarial robustness of visual transformers","author":"Shao","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00753"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00970"},{"key":"ref27","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long","year":"2017"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_29"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01247"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3106095"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3141614"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3114550"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3126404"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3080516"},{"key":"ref35","first-page":"29393","article-title":"Exploiting the intrinsic neighborhood structure for source-free domain adaptation","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Yang","year":"2021"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3110179"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref38","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. NAACL-HLT","author":"Kenton","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3046683"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1656"},{"key":"ref41","first-page":"9","article-title":"ARABERT: Transformer-based model for arabic language understanding","volume-title":"Proc. 4th Workshopon Open-Source Arabic Corpora Process. Tools, Shared Task Offensive Lang. Detection","author":"Antoun","year":"2020"},{"key":"ref42","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Touvron","year":"2021"},{"key":"ref43","first-page":"980","article-title":"Global filter networks for image classification","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Rao","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00902"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"ref50","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xie","year":"2021"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01227"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"ref54","first-page":"12116","article-title":"Do vision transformers see like convolutional neural networks?","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Raghu","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00252"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref57","article-title":"Categorical reparameterization with Gumbel-Softmax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jang","year":"2016"},{"key":"ref58","first-page":"1558","article-title":"Learning discrete representations via information maximizing self-augmented training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hu","year":"2017"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00066"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01361"},{"key":"ref64","first-page":"3635","article-title":"Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Huang","year":"2021"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00885"},{"issue":"11","key":"ref67","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10384483\/10319072.pdf?arnumber=10319072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T21:18:00Z","timestamp":1711487880000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10319072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/tmm.2023.3333190","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}