{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:51:14Z","timestamp":1771458674312,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Semi-Supervised Object Detection (SSOD) aims to improve performance by leveraging a large amount of unlabeled data. Existing works usually adopt the teacher-student framework to enforce student to learn consistent predictions over the pseudo-labels generated by teacher. However, the performance of the student model is limited since the noise inherently exists in pseudo-labels. In this paper, we investigate the causes and effects of noisy pseudo-labels and propose a simple yet effective approach denoted as Self-Correction Mean Teacher(SCMT) to reduce the adverse effects. Specifically, we propose to dynamically re-weight the unsupervised loss of each student's proposal with additional supervision information from the teacher model, and assign smaller loss weights to possible noisy proposals. Extensive experiments on MS-COCO benchmark have shown the superiority of our proposed SCMT, which can significantly improve the supervised baseline by more than 11% mAP under all 1%, 5% and 10% COCO-standard settings, and surpasses state-of-the-art methods by about 1.5% mAP. Even under the challenging COCO-additional setting, SCMT still improves the supervised baseline by 4.9% mAP, and significantly outperforms previous methods by 1.2% mAP, achieving a new state-of-the-art performance.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/207","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"1488-1494","source":"Crossref","is-referenced-by-count":6,"title":["SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection"],"prefix":"10.24963","author":[{"given":"Feng","family":"Xiong","sequence":"first","affiliation":[{"name":"Alibaba Group"}]},{"given":"Jiayi","family":"Tian","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Zhihui","family":"Hao","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Yulin","family":"He","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Xiaofeng","family":"Ren","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:08:21Z","timestamp":1658142501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/207"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/207","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}