{"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/ambiguity-resistant-semi-supervised-learning","title":"Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection","arxiv_id":"2303.14960","date":"2023-03-27","proceeding":"CVPR 2023 1","authors":["Chang Liu","Weiming Zhang","Xiangru Lin","Wei zhang","Xiao Tan","Junyu Han","Xiaomao Li","Errui Ding","Jingdong Wang"],"abstract":"With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a \"divide-and-conquer\" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://github.com/PaddlePaddle/PaddleDetection.","url_abs":"https://arxiv.org/abs/2303.14960v1","url_pdf":"https://arxiv.org/pdf/2303.14960v1.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":"ambiguity-resistant-semi-supervised-learning","repo_url":"https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/arsl","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"dense-object-detection","task_name":"Dense Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"semi-supervised-object-detection","task_name":"Semi-Supervised Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-1","task":"Semi-Supervised Object Detection","dataset":"COCO 1% labeled data","model":"ARSL","rank_in_archive_order":6,"of":22,"metrics":{"mAP":"25.36"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-10","task":"Semi-Supervised Object Detection","dataset":"COCO 10% labeled data","model":"ARSL","rank_in_archive_order":4,"of":27,"metrics":{"detector":"FCOS-Res50","mAP":"38.5"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-10","task":"Semi-Supervised Object Detection","dataset":"COCO 10% labeled data","model":"Semi-DETR","rank_in_archive_order":27,"of":27,"metrics":{"detector":"FCOS-Res50"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-2","task":"Semi-Supervised Object Detection","dataset":"COCO 2% labeled data","model":"ARSL","rank_in_archive_order":4,"of":19,"metrics":{"mAP":"29.08"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-5","task":"Semi-Supervised Object Detection","dataset":"COCO 5% labeled data","model":"ARSL","rank_in_archive_order":3,"of":23,"metrics":{"mAP":"34.45"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.14960","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}