{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:47:08Z","timestamp":1783824428158,"version":"3.55.0"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3295929","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T18:08:19Z","timestamp":1689876499000},"page":"4664-4676","source":"Crossref","is-referenced-by-count":29,"title":["Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6917-8654","authenticated-orcid":false,"given":"Zhihe","family":"Lu","sequence":"first","affiliation":[{"name":"Centre for Vision Speech and Signal Processing (CVSSP) and the iFlyTek-Surrey Joint Research Center on Artificial Intelligence, University of Surrey, Guildford, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2101-2989","authenticated-orcid":false,"given":"Da","family":"Li","sequence":"additional","affiliation":[{"name":"Samsung AI Center, Cambridge, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5908-3275","authenticated-orcid":false,"given":"Yi-Zhe","family":"Song","sequence":"additional","affiliation":[{"name":"Centre for Vision Speech and Signal Processing (CVSSP) and the iFlyTek-Surrey Joint Research Center on Artificial Intelligence, University of Surrey, Guildford, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2530-1059","authenticated-orcid":false,"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[{"name":"Centre for Vision Speech and Signal Processing (CVSSP) and the iFlyTek-Surrey Joint Research Center on Artificial Intelligence, University of Surrey, Guildford, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4867-7486","authenticated-orcid":false,"given":"Timothy M.","family":"Hospedales","sequence":"additional","affiliation":[{"name":"Samsung AI Center, Cambridge, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00221"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00710"},{"key":"ref12","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00688"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00219"},{"key":"ref58","first-page":"10","article-title":"Style augmentation: Data augmentation via style randomization","volume":"6","author":"jackson","year":"2019","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit Workshops"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00913"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00261"},{"key":"ref19","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"ref46","first-page":"642","article-title":"Classes matter: A fine-grained adversarial approach to cross-domain semantic segmentation","author":"wang","year":"2020","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00964"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_42"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889774"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_32"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00439"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00840"},{"key":"ref8","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","author":"hoffman","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref7","first-page":"3569","article-title":"Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation","volume":"33","author":"kang","year":"2020","journal-title":"Proc NIPS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_32"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2347059"},{"key":"ref3","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref6","article-title":"Unsupervised domain adaptation for semantic image segmentation: A comprehensive survey","author":"csurka","year":"2021","journal-title":"arXiv 2112 03241"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-79175-8"},{"key":"ref40","first-page":"1","article-title":"Practical variational inference for neural networks","volume":"24","author":"graves","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01395-y"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01216"},{"key":"ref36","first-page":"1","article-title":"Conservative uncertainty estimation by fitting prior networks","author":"ciosek","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_48"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"ref33","first-page":"1","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"lakshminarayanan","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"9608","article-title":"Uncertainty reduction for model adaptation in semantic segmentation","author":"fleuret","year":"2021","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248077"},{"key":"ref39","first-page":"15897","article-title":"On the expressiveness of approximate inference in Bayesian neural networks","volume":"33","author":"foong","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_72"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00154"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref67","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref20","first-page":"1","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume":"30","author":"kendall","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00686"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6952"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/150"},{"key":"ref28","first-page":"1","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"tarvainen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","first-page":"6028","article-title":"Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation","author":"liang","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref60","article-title":"Benchmarking robustness in object detection: Autonomous driving when winter is coming","author":"michaelis","year":"2019","journal-title":"arXiv 1907 07484"},{"key":"ref62","first-page":"1","article-title":"Category anchor-guided unsupervised domain adaptation for semantic segmentation","volume":"32","author":"zhang","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref61","article-title":"Imgaug","author":"jung","year":"2020"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10189399.pdf?arnumber=10189399","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T18:26:41Z","timestamp":1693852001000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10189399\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3295929","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}