{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:28:14Z","timestamp":1774538894934,"version":"3.50.1"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["62072116"],"award-info":[{"award-number":["62072116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MTC Programmatic Funds","award":["M23L7b0021"],"award-info":[{"award-number":["M23L7b0021"]}]},{"DOI":"10.13039\/100010449","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010449","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Singapore"},{"name":"MOE Academic Research Fund Tier 1 - SMU-SUTD Internal Research","award":["SMU-SUTD 2023_02_09"],"award-info":[{"award-number":["SMU-SUTD 2023_02_09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tmm.2025.3535334","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T13:54:42Z","timestamp":1737986082000},"page":"3925-3938","source":"Crossref","is-referenced-by-count":2,"title":["Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization"],"prefix":"10.1109","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0542-3482","authenticated-orcid":false,"given":"Pengkun","family":"Jiao","sequence":"first","affiliation":[{"name":"Fudan Vision and Learning Laboratory (FVL), Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2329-7014","authenticated-orcid":false,"given":"Na","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Information Systems Technology and Design, Singapore University of Technology and Design (ISTD), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-264X","authenticated-orcid":false,"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan Vision and Learning Laboratory (FVL), Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1907-8567","authenticated-orcid":false,"given":"Yu-Gang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan Vision and Learning Laboratory (FVL), Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01167"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"ref3","article-title":"Coca: Contrastive captioners are image-text foundation models","author":"Yu","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref5","first-page":"831","article-title":"Principles of risk minimization for learning theory","volume-title":"Proc. 5th Int. Conf. Neural Inf. Process. Syst.","author":"Vapnik","year":"1991"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3032060"},{"key":"ref7","first-page":"3122","article-title":"Detecting and correcting for label shift with black box predictors","volume-title":"Int. Conf. Mach. Learn.","author":"Lipton","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00950"},{"key":"ref9","article-title":"Crossmatch: Cross-classifier consistency regularization for open-set single domain generalization","volume-title":"Int. Conf. Learn. Representations","author":"Zhu","year":"2022"},{"key":"ref10","first-page":"5339","article-title":"Generalizing to unseen domains via adversarial data augmentation","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Volpi","year":"2018"},{"key":"ref11","first-page":"14435","article-title":"Maximum-entropy adversarial data augmentation for improved generalization and robustness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Zhao","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00183"},{"key":"ref13","first-page":"2825","article-title":"Explicit inductive bias for transfer learning with convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xuhong","year":"2018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2021.0068"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2920620"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3258624"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2908795"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00887"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053273"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01913-8"},{"key":"ref26","article-title":"Uncertainty modeling for out-of-distribution generalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2022"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00786"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00788"},{"key":"ref30","first-page":"10","article-title":"Domain generalization via invariant feature representation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Muandet","year":"2013"},{"key":"ref31","first-page":"322","article-title":"DIVA: Domain invariant variational autoencoders","volume-title":"Proc. Med. Imag. Deep Learn.","author":"Ilse","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3141614"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3141604"},{"key":"ref34","article-title":"MetaReg: Towards domain generalization using meta-regularization","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"31","author":"Balaji","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_12"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"ref37","first-page":"3915","article-title":"Feature-critic networks for heterogeneous domain generalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451318"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_5"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3015224"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3257566"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3070791"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00948"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"ref46","article-title":"Gradient matching for domain generalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shi","year":"2022"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3157441"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00811"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3184598"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00029"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00461"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00508"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_38"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00085"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00438"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3106743"},{"key":"ref59","article-title":"Open-set recognition: A good closed-set classifier is all you need?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Vaze","year":"2022"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3189996"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506163"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06237-1"},{"key":"ref63","article-title":"One ring to bring them all: Towards open-set recognition under domain shift","author":"Yang","year":"2022"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01062"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3058954"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19815-1_40"},{"key":"ref67","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref68","first-page":"52","article-title":"Crossnorm and selfnorm for generalization under distribution shifts","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","author":"Tang","year":"2021"},{"key":"ref69","article-title":"Domain generalization with mixstyle","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou","year":"2021"},{"key":"ref70","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"ref72","first-page":"25956","article-title":"Openmatch: Open-set semi-supervised learning with open-set consistency regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Saito","year":"2021"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"issue":"4","key":"ref75","first-page":"327","article-title":"Deep residual learning","volume":"7","author":"He","year":"2015","journal-title":"Image Recognit."},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22147-7"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383267"},{"issue":"11","key":"ref79","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6046\/10844992\/10855521.pdf?arnumber=10855521","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T17:48:44Z","timestamp":1752169724000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10855521\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":79,"URL":"https:\/\/doi.org\/10.1109\/tmm.2025.3535334","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}