{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:59:20Z","timestamp":1775325560003,"version":"3.50.1"},"reference-count":50,"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":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61991451"],"award-info":[{"award-number":["61991451"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302369"],"award-info":[{"award-number":["62302369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M742737"],"award-info":[{"award-number":["2023M742737"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tip.2024.3352392","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T18:36:18Z","timestamp":1705516578000},"page":"856-866","source":"Crossref","is-referenced-by-count":8,"title":["TransVQA: Transferable Vector Quantization Alignment for Unsupervised Domain Adaptation"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9917-0269","authenticated-orcid":false,"given":"Yulin","family":"Sun","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9632-985X","authenticated-orcid":false,"given":"Weisheng","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2067-2763","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University at Albany, Albany, NY, USA"}]},{"given":"Le","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2179-3292","authenticated-orcid":false,"given":"Guangming","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-0845","authenticated-orcid":false,"given":"Xuemei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref4","article-title":"An image is worth 16\u271716 words: Transformers for image recognition at scale","volume-title":"Proc. ICLR","author":"Dosovitskiy"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref7","article-title":"Domain adaptation for visual applications: A comprehensive survey","author":"Csurka","year":"2017","journal-title":"arXiv:1702.05374"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5943"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.3028503"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2020.3004555"},{"key":"ref11","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref12","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00392"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00897"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"ref16","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0069"},{"key":"ref18","first-page":"7404","article-title":"Bridging theory and algorithm for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref19","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Long"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2988928"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6801"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00059"},{"key":"ref27","article-title":"CDTrans: Cross-domain transformer for unsupervised domain adaptation","volume-title":"Proc. ICLR","author":"Xu"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43424-2_19"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351070"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3103390"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00115"},{"key":"ref33","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Vaswani"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548229"},{"key":"ref36","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"ref37","first-page":"6306","article-title":"Neural discrete representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Van Den Oord"},{"key":"ref38","first-page":"14837","article-title":"Generating diverse high-fidelity images with VQ-VAE-2","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Razavi"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190973"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109065"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01135"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref43","article-title":"VisDA: The visual domain adaptation challenge","author":"Peng","year":"2017","journal-title":"arXiv:1710.06924"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.572"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref48","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv:1711.05101"},{"issue":"11","key":"ref49","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"ref50","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/10346232\/10403784.pdf?arnumber=10403784","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T16:33:31Z","timestamp":1706027611000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10403784\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tip.2024.3352392","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}