{"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/fcns-in-the-wild-pixel-level-adversarial-and","title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","arxiv_id":"1612.02649","date":"2016-12-08","proceeding":null,"authors":["Judy Hoffman","Dequan Wang","Fisher Yu","Trevor Darrell"],"abstract":"Fully convolutional models for dense prediction have proven successful for a\nwide range of visual tasks. Such models perform well in a supervised setting,\nbut performance can be surprisingly poor under domain shifts that appear mild\nto a human observer. For example, training on one city and testing on another\nin a different geographic region and/or weather condition may result in\nsignificantly degraded performance due to pixel-level distribution shift. In\nthis paper, we introduce the first domain adaptive semantic segmentation\nmethod, proposing an unsupervised adversarial approach to pixel prediction\nproblems. Our method consists of both global and category specific adaptation\ntechniques. Global domain alignment is performed using a novel semantic\nsegmentation network with fully convolutional domain adversarial learning. This\ninitially adapted space then enables category specific adaptation through a\ngeneralization of constrained weak learning, with explicit transfer of the\nspatial layout from the source to the target domains. Our approach outperforms\nbaselines across different settings on multiple large-scale datasets, including\nadapting across various real city environments, different synthetic\nsub-domains, from simulated to real environments, and on a novel large-scale\ndash-cam dataset.","url_abs":"http://arxiv.org/abs/1612.02649v1","url_pdf":"http://arxiv.org/pdf/1612.02649v1.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":"fcns-in-the-wild-pixel-level-adversarial-and","repo_url":"https://github.com/Wanger-SJTU/FCN-in-the-wild","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}},{"paper_slug":"fcns-in-the-wild-pixel-level-adversarial-and","repo_url":"https://github.com/stu92054/Domain-adaptation-on-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fcns-in-the-wild-pixel-level-adversarial-and","repo_url":"https://github.com/zsano1/MEnet-FCN-wild","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"synthetic-to-real-translation","task_name":"Synthetic-to-Real Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-synthia-fall-to","task":"Image-to-Image Translation","dataset":"SYNTHIA Fall-to-Winter","model":"FCNs in the wild","rank_in_archive_order":2,"of":2,"metrics":{"mIoU":"59.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-synthia-to","task":"Image-to-Image Translation","dataset":"SYNTHIA-to-Cityscapes","model":"FCNs in the wild","rank_in_archive_order":28,"of":28,"metrics":{"mIoU (13 classes)":"20.2"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-gtav-to","task":"Synthetic-to-Real Translation","dataset":"GTAV-to-Cityscapes Labels","model":"FCNs in the wild","rank_in_archive_order":73,"of":73,"metrics":{"mIoU":"27.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.02649","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}