{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:11:07Z","timestamp":1778166667181,"version":"3.51.4"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tnnls.2022.3212909","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T21:20:01Z","timestamp":1667510401000},"page":"6767-6778","source":"Crossref","is-referenced-by-count":28,"title":["Aligning Correlation Information for Domain Adaptation in Action Recognition"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4292-7379","authenticated-orcid":false,"given":"Yuecong","family":"Xu","sequence":"first","affiliation":[{"name":"Agency for Science, Technology and Research (A*STAR), Institute for Infocomm Research (I2R), Fusionopolis, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7703-3490","authenticated-orcid":false,"given":"Haozhi","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9191-8604","authenticated-orcid":false,"given":"Kezhi","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Agency for Science, Technology and Research (A*STAR), Institute for Infocomm Research (I2R) and the Centre for Frontier AI Research (CFAR), Fusionopolis, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7137-4136","authenticated-orcid":false,"given":"Lihua","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8075-0439","authenticated-orcid":false,"given":"Jianfei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2330900"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3028503"},{"key":"ref3","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2902100"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3016180"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01172"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3046868"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3001522"},{"key":"ref10","article-title":"Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification","author":"Ge","year":"2020","journal-title":"arXiv:2001.01526"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00642"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6854"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01058"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_40"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.38"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.05.028"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3037496"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107486"},{"key":"ref21","first-page":"352","article-title":"A2-Nets: Double attention networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00155"},{"key":"ref23","first-page":"6510","article-title":"Compact generalized non-local network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yue"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107980"},{"key":"ref25","volume-title":"Mathematical Statistics and Data Analysis","author":"Rice","year":"2006"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2201748"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref29","article-title":"ARID: A new dataset for recognizing action in the dark","author":"Xu","year":"2020","journal-title":"arXiv:2006.03876"},{"key":"ref30","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Simonyan"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.787"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.368"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2749159"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_23"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868668"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.01.020"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref38","article-title":"ConvNet architecture search for spatiotemporal feature learning","author":"Tran","year":"2017","journal-title":"arXiv:1708.05038"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12333"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00685"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.028"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2965434"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00710"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_49"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2885799"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref51","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015997"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00078"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00746"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00189"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107764"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106991"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240552"},{"key":"ref62","first-page":"264","article-title":"Deep domain adaptation in action space","volume-title":"Proc. BMVC","author":"Jamal"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00947"},{"key":"ref64","first-page":"425","article-title":"Domain adaptation for relation extraction with domain adversarial neural network","volume-title":"Proc. 8th Int. Joint Conf. Natural Lang. Process.","volume":"2","author":"Fu"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/310"},{"key":"ref66","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.103"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2016.01.001"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1212.0402"},{"key":"ref71","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_22"},{"key":"ref73","article-title":"The kinetics human action video dataset","author":"Kay","year":"2017","journal-title":"arXiv:1705.06950"},{"key":"ref74","first-page":"177","article-title":"Large-scale machine learning with stochastic gradient descent","volume-title":"Proceedings of COMPSTAT\u20192010","author":"Bottou"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00151"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"issue":"11","key":"ref78","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 Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10517792\/09923421.pdf?arnumber=9923421","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T18:49:05Z","timestamp":1714762145000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9923421\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":78,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3212909","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}