{"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/domain-adaptive-faster-r-cnn-for-object","title":"Domain Adaptive Faster R-CNN for Object Detection in the Wild","arxiv_id":"1803.03243","date":"2018-03-08","proceeding":"CVPR 2018 6","authors":["Yuhua Chen","Wen Li","Christos Sakaridis","Dengxin Dai","Luc van Gool"],"abstract":"Object detection typically assumes that training and test data are drawn from\nan identical distribution, which, however, does not always hold in practice.\nSuch a distribution mismatch will lead to a significant performance drop. In\nthis work, we aim to improve the cross-domain robustness of object detection.\nWe tackle the domain shift on two levels: 1) the image-level shift, such as\nimage style, illumination, etc, and 2) the instance-level shift, such as object\nappearance, size, etc. We build our approach based on the recent\nstate-of-the-art Faster R-CNN model, and design two domain adaptation\ncomponents, on image level and instance level, to reduce the domain\ndiscrepancy. The two domain adaptation components are based on H-divergence\ntheory, and are implemented by learning a domain classifier in adversarial\ntraining manner. The domain classifiers on different levels are further\nreinforced with a consistency regularization to learn a domain-invariant region\nproposal network (RPN) in the Faster R-CNN model. We evaluate our newly\nproposed approach using multiple datasets including Cityscapes, KITTI, SIM10K,\netc. The results demonstrate the effectiveness of our proposed approach for\nrobust object detection in various domain shift scenarios.","url_abs":"http://arxiv.org/abs/1803.03243v1","url_pdf":"http://arxiv.org/pdf/1803.03243v1.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":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/yuhuayc/da-faster-rcnn","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/harsh-99/SCL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/jinlong17/da-detect","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/krumo/Detectron-DA-Faster-RCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/krumo/Domain-Adaptive-Faster-RCNN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/shreyasrajesh/DA-Object-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/GiovanniPasq/DA-Faster-RCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"domain-adaptive-faster-r-cnn-for-object","repo_url":"https://github.com/tiancity-NJU/da-faster-rcnn-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"robust-object-detection","task_name":"Robust Object Detection"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-cityscapes-to","task":"Image-to-Image Translation","dataset":"Cityscapes-to-Foggy Cityscapes","model":"FRCNN in the wild","rank_in_archive_order":6,"of":6,"metrics":{"mAP":"27.6"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-cityscapes-1","task":"Unsupervised Domain Adaptation","dataset":"Cityscapes to Foggy Cityscapes","model":"DA-Faster","rank_in_archive_order":22,"of":22,"metrics":{"mAP@0.5":"26.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.03243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.03243"}},"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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