{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-domain-adaptation-for-semantic","title":"Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Yang Zou","Zhiding Yu","B. V. K. Vijaya Kumar","Jinsong Wang"],"abstract":"Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks. Despite such progress, thesemodels often face challenges in real world “wild tasks” where large differ-ence between labeled training/source data and unseen test/target dataexists. In particular, such difference is often referred to as “domain gap”,and  could  cause  significantly  decreased  performance  which  cannot  beeasily remedied by further increasing the representation power. Unsuper-vised domain adaptation (UDA) seeks to overcome such problem withouttarget domain labels. In this paper, we propose a novel UDA frameworkbased  on  an  iterative  self-training  (ST)  procedure,  where  the  problemis formulated as latent variable loss minimization, and can be solved byalternatively generating pseudo labels on target data and re-training themodel with these labels. On top of ST, we also propose a novel class-balanced  self-training  (CBST)  framework  to  avoid  the  gradual  domi-nance of large classes on pseudo-label generation, and introduce spatialpriors to refine generated labels. Comprehensive experiments show thatthe  proposed  methods  achieve  state  of  the  art  semantic  segmentationperformance under multiple major UDA settings.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-domain-adaptation-for-semantic","repo_url":"https://github.com/yzou2/CBST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mxnet","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-gtav-to","task":"Image-to-Image Translation","dataset":"GTAV-to-Cityscapes Labels","model":"CBST","rank_in_archive_order":18,"of":22,"metrics":{"mIoU":"47.0"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-23","task":"Semi-Supervised Semantic Segmentation","dataset":"ScribbleKITTI","model":"CBST (Range View)","rank_in_archive_order":6,"of":9,"metrics":{"mIoU (1% Labels)":"35.7","mIoU (10% Labels)":"50.7","mIoU (20% Labels)":"52.7","mIoU (50% Labels)":"54.6"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-24","task":"Semi-Supervised Semantic Segmentation","dataset":"SemanticKITTI","model":"CBST (Range View)","rank_in_archive_order":9,"of":12,"metrics":{"mIoU (1% Labels)":"39.9","mIoU (10% Labels)":"53.4","mIoU (20% Labels)":"56.1","mIoU (50% Labels)":"56.9"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-25","task":"Semi-Supervised Semantic Segmentation","dataset":"nuScenes","model":"CBST (Range View)","rank_in_archive_order":9,"of":11,"metrics":{"mIoU (1% Labels)":"40.9","mIoU (10% Labels)":"60.5","mIoU (20% Labels)":"64.3","mIoU (50% Labels)":"69.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}