{"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/unbiased-teacher-v2-semi-supervised-object-1","title":"Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors","arxiv_id":"2206.09500","date":"2022-06-19","proceeding":"CVPR 2022 1","authors":["Yen-Cheng Liu","Chih-Yao Ma","Zsolt Kira"],"abstract":"With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are still two challenges that are not addressed: (1) there is no prior SS-OD work on anchor-free detectors, and (2) prior works are ineffective when pseudo-labeling bounding box regression. In this paper, we present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors and also introduces Listen2Student mechanism for the unsupervised regression loss. Specifically, we first present a study examining the effectiveness of existing SS-OD methods on anchor-free detectors and find that they achieve much lower performance improvements under the semi-supervised setting. We also observe that box selection with centerness and the localization-based labeling used in anchor-free detectors cannot work well under the semi-supervised setting. On the other hand, our Listen2Student mechanism explicitly prevents misleading pseudo-labels in the training of bounding box regression; we specifically develop a novel pseudo-labeling selection mechanism based on the Teacher and Student's relative uncertainties. This idea contributes to favorable improvement in the regression branch in the semi-supervised setting. Our method, which works for both anchor-free and anchor-based methods, consistently performs favorably against the state-of-the-art methods in VOC, COCO-standard, and COCO-additional.","url_abs":"https://arxiv.org/abs/2206.09500v1","url_pdf":"https://arxiv.org/pdf/2206.09500v1.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":"unbiased-teacher-v2-semi-supervised-object-1","repo_url":"https://github.com/facebookresearch/unbiased-teacher-v2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semi-supervised-object-detection","task_name":"Semi-Supervised Object Detection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-0-5","task":"Semi-Supervised Object Detection","dataset":"COCO 0.5% labeled data","model":"Unbiased Teacher v2","rank_in_archive_order":1,"of":5,"metrics":{"mAP":"21.26 ± 0.21"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-1","task":"Semi-Supervised Object Detection","dataset":"COCO 1% labeled data","model":"Unbiased Teacher v2","rank_in_archive_order":4,"of":22,"metrics":{"mAP":"26.07±0.36"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-10","task":"Semi-Supervised Object Detection","dataset":"COCO 10% labeled data","model":"Unbiased Teacher v2","rank_in_archive_order":12,"of":27,"metrics":{"detector":"FCOS-Res50","mAP":"35.08±0.02"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-2","task":"Semi-Supervised Object Detection","dataset":"COCO 2% labeled data","model":"Unbiased Teacher v2","rank_in_archive_order":7,"of":19,"metrics":{"mAP":"28.37±0.03"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-5","task":"Semi-Supervised Object Detection","dataset":"COCO 5% labeled data","model":"Unbiased Teacher v2","rank_in_archive_order":12,"of":23,"metrics":{"mAP":"31.85±0.09"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.09500","atlas_url":"https://app.syntology.ai/?focus=2206.09500","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}