"Unbiased Teacher for Semi-Supervised Object Detection" is a research paper accepted to the 9th annual International Conference on Learning Representations (ICLR). This work was conducted by Yen-Cheng Liu (Georgia Tech), Chih-Yao Ma (Facebook), Zijian He (Facebook), Chia-Wen Kuo (Georgia Tech), Kan Chen (Facebook), Peizhao Zhang (Facebook), Bichen Wu (Facebook), Zsolt Kira (Georgia Tech), Peter Vajda (Facebook).
Abstract:
Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on image classification tasks and neglected object detection which requires more annotation effort. In this work, we revisit the Semi-Supervised Object Detection (SS-OD) and identify the pseudo-labeling bias issue in SS-OD. To address this, we introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner. Together with a class-balance loss to downweight overly confident pseudo-labels, Unbiased Teacher consistently improved state-of-the-art methods by significant margins on COCO-standard, COCO-additional, and VOC datasets. Specifically, Unbiased Teacher achieves 6.8 absolute mAP improvements against state-of-the-art method when using 1% of labeled data on MS-COCO, achieves around 10 mAP improvements against the supervised baseline when using only 0.5, 1, 2% of labeled data on MS-COCO.
Full paper available at [ Ссылка ]_
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