{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:20:45Z","timestamp":1761582045497,"version":"3.37.3"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802266"],"award-info":[{"award-number":["61802266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3049552","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T09:08:18Z","timestamp":1610010498000},"page":"9154-9162","source":"Crossref","is-referenced-by-count":4,"title":["Exploring Category Attention for Open Set Domain Adaptation"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2629-1198","authenticated-orcid":false,"given":"Jinghua","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc NIPS"},{"key":"ref38","first-page":"89","article-title":"Image inpainting for irregular holes using partial convolutions","volume":"11215","author":"liu","year":"2018","journal-title":"Proc ECCV"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6822"},{"key":"ref32","first-page":"1","article-title":"Pay less attention with lightweight and dynamic convolutions","author":"wu","year":"2019","journal-title":"Proc ICLR"},{"key":"ref31","first-page":"1243","article-title":"Learning to combine foveal glimpses with a third-order Boltzmann machine","author":"larochelle","year":"2010","journal-title":"Proc NIPS"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00304"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6807"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6710"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6907"},{"key":"ref28","first-page":"393","article-title":"Multi-class open set recognition using probability of inclusion","volume":"8691","author":"lalit jain","year":"2014","journal-title":"Proc ECCV"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6091"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref1","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.88"},{"key":"ref22","first-page":"12373","article-title":"Towards inheritable models for open-set domain adaptation","author":"nath kundu","year":"2020","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01388"},{"key":"ref24","first-page":"153","article-title":"Open set domain adaptation by backpropagation","author":"saito","year":"2018","journal-title":"Proc ECCV"},{"key":"ref23","first-page":"1","article-title":"Learning factorized representations for open-set domain adaptation","author":"baktashmotlagh","year":"2019","journal-title":"Proc ICLR"},{"key":"ref26","first-page":"1","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"zellinger","year":"2017","journal-title":"Proc ICLR"},{"key":"ref25","first-page":"3","article-title":"CBAM: Convolutional block attention module","volume":"11211","author":"woo","year":"2018","journal-title":"Proc ECCV"},{"key":"ref50","first-page":"384","article-title":"Learning a parametric embedding by preserving local structure","volume":"5","author":"maaten","year":"2009","journal-title":"Proc 12th Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref51","article-title":"Randaugment: Practical data augmentation with no separate search","author":"cubuk","year":"2019","journal-title":"arXiv 1909 13719"},{"key":"ref57","first-page":"513","article-title":"A kernel method for the two-sample-problem","author":"gretton","year":"2006","journal-title":"Proc NIPS"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref56"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref52","first-page":"213","article-title":"Adapting visual category models to new domains","volume":"6314","author":"saenko","year":"2010","journal-title":"Proc ECCV"},{"key":"ref10","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume":"37","author":"long","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref11","first-page":"1994","article-title":"Cycada: Cycle-consistent adversarial domain adaptation","volume":"80","author":"hoffman","year":"2018","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00845"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015997"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015401"},{"key":"ref17","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume":"37","author":"ganin","year":"2015","journal-title":"Proc ICML"},{"key":"ref18","first-page":"136","article-title":"Unsupervised domain adaptation with residual transfer networks","author":"long","year":"2016","journal-title":"Proc NIPS"},{"key":"ref19","first-page":"1","article-title":"Generative OpenMax for multi-class open set classification","author":"ge","year":"2017","journal-title":"Proc Brit Mach Vis Conf"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5943"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1609\/aaai.v34i04.5757","article-title":"Adversarial-learned loss for domain adaptation","author":"chen","year":"2020","journal-title":"Proc AAAI"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6137"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.107"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093333"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018247"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301305"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref43","first-page":"818","article-title":"Visualizing and understanding convolutional networks","volume":"8689","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis (ECCV)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09316151.pdf?arnumber=9316151","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T16:23:07Z","timestamp":1643214187000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9316151\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3049552","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}