{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:01:01Z","timestamp":1781247661853,"version":"3.54.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Ministry of S&T of China","award":["2025ZD0123800"],"award-info":[{"award-number":["2025ZD0123800"]}]},{"DOI":"10.13039\/501100003397","name":"HUST","doi-asserted-by":"publisher","award":["2025JCYJ077"],"award-info":[{"award-number":["2025JCYJ077"]}],"id":[{"id":"10.13039\/501100003397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tip.2026.3689419","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:51:34Z","timestamp":1778615494000},"page":"6113-6122","source":"Crossref","is-referenced-by-count":0,"title":["Frequency-Aware Domain Generalization"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0606-6193","authenticated-orcid":false,"given":"Xiang","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6822-1371","authenticated-orcid":false,"given":"Jing","family":"Ma","sequence":"additional","affiliation":[{"name":"School of AI and Automation, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanlin","family":"Li","sequence":"additional","affiliation":[{"name":"School of AI and Automation, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0421-8416","authenticated-orcid":false,"given":"Trac D.","family":"Tran","sequence":"additional","affiliation":[{"name":"Department of ECE, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"ref2","article-title":"Domain-invariant feature exploration for domain generalization","author":"Lu","year":"2022","journal-title":"arXiv:2207.12020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_1"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128"},{"key":"ref5","article-title":"In search of lost domain generalization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Gulrajani"},{"key":"ref6","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"ref8","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref10","first-page":"1","article-title":"Domain generalization with mixstyle","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhou"},{"key":"ref11","first-page":"1","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref12","first-page":"1170","article-title":"A theory of label propagation for subpopulation shift","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cai"},{"key":"ref13","first-page":"23519","article-title":"Towards a theoretical framework of out-of-distribution generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Ye"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.1967.5217220"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00381"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00144"},{"key":"ref19","article-title":"Invariant risk minimization","author":"Arjovsky","year":"2019","journal-title":"arXiv:1907.02893"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"ref21","article-title":"Gradient matching for domain generalization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Shi"},{"key":"ref22","first-page":"18347","article-title":"Fishr: Invariant gradient variances for out-of-distribution generalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rame"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_26"},{"key":"ref24","first-page":"22405","article-title":"SWAD: Domain generalization by seeking flat minima","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Cha"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0601"},{"key":"ref26","first-page":"11539","article-title":"Improving robustness against common corruptions by covariate shift adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Schneider"},{"key":"ref27","first-page":"9229","article-title":"Test-time training with self-supervision for generalization under distribution shifts","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sun"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00092"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i4.28076"},{"key":"ref30","first-page":"1","article-title":"Learning with local and global consistency","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"16","author":"Zhou"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref34","article-title":"Generalizing across domains via cross-gradient training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Shankar"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01135"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02315"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00153"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"ref41","first-page":"1","article-title":"Adversarial teacher\u2013student representation learning for domain generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Yang"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00811"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00787"},{"key":"ref45","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02313"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01030"},{"key":"ref55","first-page":"1","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hendrycks"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref58","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref60","first-page":"1","article-title":"MetaReg: Towards domain generalization using meta-regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Balaji"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/11355710\/11515015.pdf?arnumber=11515015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:26:05Z","timestamp":1781245565000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11515015\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/tip.2026.3689419","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}