SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection

SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection

Feng Xiong, Jiayi Tian, Zhihui Hao, Yulin He, Xiaofeng Ren

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1488-1494. https://doi.org/10.24963/ijcai.2022/207

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.
Keywords:
Computer Vision: Recognition (object detection, categorization)
Computer Vision: Transfer, low-shot, semi- and un- supervised learning