Humble Teachers Teach Better Students for Semi-Supervised Object Detection

Yihe Tang, Weifeng Chen, Yijun Luo, Yuting Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 3132-3141

Abstract


We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework. Our method is featured with 1) the exponential moving averaging strategy to update the teacher from the student online, 2) using plenty of region proposals and soft pseudo-labels as the student's training targets, and 3) a light-weighted detection-specific data ensemble for the teacher to generate more reliable pseudo labels. Compared to the recent state-of-the-art - STAC, which uses hard labels on sparsely selected hard pseudo samples, the teacher in our model exposes richer information to the student with soft-labels on many proposals. Our model achieves COCO-style AP of 53.04% on VOC07 val set, 8.4% better than STAC, when using VOC12 as unlabeled data. On MS-COCO, it outperforms prior work when only a small percentage of data is taken as labeled. It also reaches 53.8% AP on MS-COCO test-dev with 3.1% gain over the fully supervised ResNet-152 cascaded R-CNN, by tapping into unlabeled data of a similar size to the labeled data.

Related Material


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[bibtex]
@InProceedings{Tang_2021_CVPR, author = {Tang, Yihe and Chen, Weifeng and Luo, Yijun and Zhang, Yuting}, title = {Humble Teachers Teach Better Students for Semi-Supervised Object Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {3132-3141} }