{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:46:32Z","timestamp":1780580792387,"version":"3.54.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) Grant funded by the Korean Government","doi-asserted-by":"publisher","award":["2020R1C1C1009662"],"award-info":[{"award-number":["2020R1C1C1009662"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Institute of Police Technology (KIPoT) Grant funded by the Korea Government (KNPA)","award":["092021D75000000"],"award-info":[{"award-number":["092021D75000000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3202190","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T19:38:40Z","timestamp":1661542720000},"page":"91137-91149","source":"Crossref","is-referenced-by-count":23,"title":["Pseudo Label Rectification via Co-Teaching and Decoupling for Multisource Domain Adaptation in Semantic Segmentation"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1244-2044","authenticated-orcid":false,"given":"So Jeong","family":"Park","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hae Ju","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Su","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1818-9203","authenticated-orcid":false,"given":"Ba Hung","family":"Ngo","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho Sub","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Engineering, Daegu University, Gyeongsan-si, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4251-7131","authenticated-orcid":false,"given":"Sung In","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"Chen","year":"2014","journal-title":"arXiv:1412.7062"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref3","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"Chen","year":"2017","journal-title":"arXiv:1706.05587"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref6","article-title":"Multi-scale context aggregation by dilated convolutions","author":"Yu","year":"2015","journal-title":"arXiv:1511.07122"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01086"},{"key":"ref8","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00154"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_38"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"ref14","first-page":"1","article-title":"Adversarial multiple source domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Zhao"},{"key":"ref15","first-page":"7287","article-title":"Multi-source domain adaptation for semantic segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhao"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00176"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref19","article-title":"Multi-source domain adaptation and semi-supervised domain adaptation with focus on visual domain adaptation challenge 2019","author":"Pan","year":"2019","journal-title":"arXiv:1910.03548"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00184"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3112012"},{"issue":"2","key":"ref22","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop challenges Represent. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00155"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00712"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00107"},{"key":"ref28","article-title":"Semantic segmentation based unsupervised domain adaptation via pseudo-label fusion","volume-title":"Proc. ICLR","author":"Chao"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_33"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_32"},{"key":"ref31","article-title":"Multiple fusion adaptation: A strong framework for unsupervised semantic segmentation adaptation","author":"Zhang","year":"2021","journal-title":"arXiv:2112.00295"},{"key":"ref32","first-page":"433","article-title":"Category anchor-guided unsupervised domain adaptation for semantic segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhang"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00896"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01395-y"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_18"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00686"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093626"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref40","first-page":"1","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Han"},{"key":"ref41","first-page":"1","article-title":"Decoupling `when to update from `how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Malach"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3136567"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3110605"},{"key":"ref45","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016","journal-title":"arXiv:1610.02242"},{"key":"ref46","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00710"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_327"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref51","article-title":"Synscapes: A photorealistic synthetic dataset for street scene parsing","author":"Wrenninge","year":"2018","journal-title":"arXiv:1810.08705"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref56","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09868789.pdf?arnumber=9868789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T02:01:05Z","timestamp":1710381665000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9868789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3202190","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}