{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:47:12Z","timestamp":1783824432194,"version":"3.55.0"},"reference-count":113,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"MUR PNRR project FAIR","award":["PE00000013"],"award-info":[{"award-number":["PE00000013"]}]},{"DOI":"10.13039\/100031478","name":"NextGenerationEU","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031478","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU Horizon projects ELIAS","award":["101120237"],"award-info":[{"award-number":["101120237"]}]},{"name":"ELLIOT","award":["101214398"],"award-info":[{"award-number":["101214398"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tpami.2025.3596943","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T18:39:39Z","timestamp":1754678379000},"page":"10378-10395","source":"Crossref","is-referenced-by-count":8,"title":["SeCoV2: Semantic Connectivity-Driven Pseudo-Labeling for Robust Cross-Domain Semantic Segmentation"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9880-8822","authenticated-orcid":false,"given":"Dong","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2179-8301","authenticated-orcid":false,"given":"Nan","family":"Pu","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4940-1211","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6597-7248","authenticated-orcid":false,"given":"Nicu","family":"Sebe","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-0544","authenticated-orcid":false,"given":"Zhun","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref2","first-page":"19769","article-title":"Segment everything everywhere all at once","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zou"},{"key":"ref3","article-title":"Fast segment anything","author":"Zhao","year":"2023"},{"key":"ref4","article-title":"Unsupervised domain adaptation for semantic image segmentation: A comprehensive survey","author":"Csurka","year":"2021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3237740"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611708"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00747"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01501"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00300"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref14","article-title":"SAM 2: Segment anything in images and videos","author":"Ravi","year":"2024"},{"key":"ref15","article-title":"Semantic segment anything","author":"Chen","year":"2023"},{"key":"ref16","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref17","first-page":"78936","article-title":"Connectivity-driven pseudo-labeling makes stronger cross-domain segmenters","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3522936"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02211"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72933-1_6"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3222634"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3229207"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73033-7_24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3320613"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01758"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01744"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72980-5_8"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3248294"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01081"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01129"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02210"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00744"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3415963"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3246392"},{"key":"ref41","first-page":"52014","article-title":"Generalize or detect? Towards robust semantic segmentation under multiple distribution shifts","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Gao"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3432168"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01395-y"},{"key":"ref44","first-page":"4749","article-title":"Learning from future: A novel self-training framework for semantic segmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Du"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3295929"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3552484"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00392"},{"key":"ref48","first-page":"15105","article-title":"Deliberated domain bridging for domain adaptive semantic segmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73397-0_1"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72933-1_19"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01991"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19830-4_3"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02217-1"},{"key":"ref55","first-page":"6690","article-title":"When visual prompt tuning meets source-free domain adaptive semantic segmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ma"},{"key":"ref56","first-page":"30726","article-title":"Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"He"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01844"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72775-7_4"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2025.3587430"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3344754"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02704"},{"key":"ref62","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref63","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Bai"},{"key":"ref64","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","author":"Li","year":"2020"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01122"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02305"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/79.543975"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"ref70","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref71","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2014.2320500"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2651400"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01262"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"ref77","first-page":"3635","article-title":"Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01129"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00784"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01081"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2903401"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73337-6_25"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72995-9_26"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01511-6"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref89","article-title":"A2D2: AEV autonomous driving dataset","author":"Geyer","year":"2020"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00939"},{"key":"ref91","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Brown"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref94","article-title":"2018 robotic scene segmentation challenge","author":"Allan","year":"2020"},{"key":"ref95","article-title":"Isprs 2D semantic labeling challenge","year":"2018"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3271776"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01551"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02260"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3419055"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00682"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3159589"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00702"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547990"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547987"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612013"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25448"},{"key":"ref109","first-page":"101223","article-title":"UniDSeg: Unified cross-domain 3D semantic segmentation via visual foundation models prior","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref110","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01401"},{"key":"ref112","article-title":"Divide to adapt: Mitigating confirmation bias for domain adaptation of black-box predictors","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yang"},{"issue":"11","key":"ref113","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 Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11192800\/11120362.pdf?arnumber=11120362","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T17:36:57Z","timestamp":1759772217000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11120362\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":113,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3596943","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}