{"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/cross-view-regularization-for-domain-adaptive","title":"Cross-View Regularization for Domain Adaptive Panoptic Segmentation","arxiv_id":"2103.02584","date":"2021-03-03","proceeding":"CVPR 2021 1","authors":["Jiaxing Huang","Dayan Guan","Aoran Xiao","Shijian Lu"],"abstract":"Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. However, most existing research was conducted under a supervised learning setup whereas unsupervised domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency leverages geometric invariance across the same image of the different styles which fabricates certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art.","url_abs":"https://arxiv.org/abs/2103.02584v1","url_pdf":"https://arxiv.org/pdf/2103.02584v1.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":"cross-view-regularization-for-domain-adaptive","repo_url":"https://github.com/jxhuang0508/CVRN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-panoptic-synthia-to","task":"Domain Adaptation","dataset":"Panoptic SYNTHIA-to-Cityscapes","model":"CVRN","rank_in_archive_order":4,"of":5,"metrics":{"mPQ":"32.1"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-panoptic-synthia-to-1","task":"Domain Adaptation","dataset":"Panoptic SYNTHIA-to-Mapillary","model":"CVRN","rank_in_archive_order":3,"of":5,"metrics":{"mPQ":"21.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.02584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.02584"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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