{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:08:09Z","timestamp":1776107289508,"version":"3.50.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"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":["62276170"],"award-info":[{"award-number":["62276170"]}],"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":["82261138629"],"award-info":[{"award-number":["82261138629"]}],"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":["62306061"],"award-info":[{"award-number":["62306061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of Guangdong Province","award":["2023A1515011549"],"award-info":[{"award-number":["2023A1515011549"]}]},{"name":"Science and Technology Project of Guangdong Province","award":["2023A1515010688"],"award-info":[{"award-number":["2023A1515010688"]}]},{"name":"Science and Technology Innovation Commission of Shenzhen","award":["JCYJ20220531101412030"],"award-info":[{"award-number":["JCYJ20220531101412030"]}]},{"name":"Science and Technology Innovation Commission of Shenzhen","award":["JCYJ20210324094602007"],"award-info":[{"award-number":["JCYJ20210324094602007"]}]},{"name":"Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy","award":["GML-KF-24-11"],"award-info":[{"award-number":["GML-KF-24-11"]}]},{"name":"Guangdong Provincial Key Laboratory","award":["2023B1212060076"],"award-info":[{"award-number":["2023B1212060076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tetci.2025.3548727","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:56:18Z","timestamp":1743029778000},"page":"4258-4272","source":"Crossref","is-referenced-by-count":1,"title":["Generalization-Enhanced Feature-Wise and Correlation Attentions for Cross-Database Facial Expression Recognition"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8946-7472","authenticated-orcid":false,"given":"Weicheng","family":"Xie","sequence":"first","affiliation":[{"name":"Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5886-1864","authenticated-orcid":false,"given":"Tao","family":"Zhong","sequence":"additional","affiliation":[{"name":"Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2339-5685","authenticated-orcid":false,"given":"Siyang","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Exeter, Exeter, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0422-6616","authenticated-orcid":false,"given":"Zitong","family":"Yu","sequence":"additional","affiliation":[{"name":"Computer Science, Great Bay University, Dongguan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1420-0815","authenticated-orcid":false,"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[{"name":"Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Domain generalization with mixstyle","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou","year":"2021"},{"key":"ref2","article-title":"Domain-invariant feature exploration for domain generalization","author":"Lu","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref6","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dosovitskiy","year":"2021"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3136606"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-022-0271-y"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00124"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00106"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2021.3070713"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00053"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3199394"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3093397"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26313-2_33"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3211006"},{"key":"ref19","first-page":"13937","article-title":"DynamicViT: Efficient vision transformers with dynamic token sparsification","volume-title":"Proc. 35th Int. Conf. Neural Inf. Process. Syst.","volume":"34","author":"Rao","year":"2021"},{"issue":"1","key":"ref20","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref21","first-page":"10750","article-title":"DropBlock: A regularization method for convolutional networks","volume":"31","author":"Ghiasi","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018425"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02174"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/app12157682"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108117"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00029"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26328"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2930359"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.12.037"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2021.3122146"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00358"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3140693"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357999"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.5244\/C.35.168"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref39","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2868382"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2993148.2993165"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2829994"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2740923"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1055-1"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/AFGR.1998.670949"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/MMUL.2012.26"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3591106.3592263"},{"key":"ref49","first-page":"38204","article-title":"Sequencer: Deep LSTM for image classification","author":"Tatsunami","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2973158"},{"key":"ref52","first-page":"3186","article-title":"Unbalanced minibatch optimal transport; applications to domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fatras","year":"2021"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548289"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25498"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ACAIT60137.2023.10528548"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103684"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20850"},{"key":"ref58","article-title":"Free lunch for domain adversarial training: Environment label smoothing","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2023"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2750679"},{"issue":"1","key":"ref60","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","author":"Maaten","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_24"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3104379"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.277"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ACII59096.2023.10388093"},{"key":"ref65","first-page":"18378","article-title":"A closer look at smoothness in domain adversarial training","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Rangwani","year":"2022"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7433297\/11267152\/10938315.pdf?arnumber=10938315","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T19:06:07Z","timestamp":1764183967000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10938315\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":65,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2025.3548727","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}