Unbiased Teacher for Semi-Supervised Object DetectionDownload PDF

Published: 12 Jan 2021, Last Modified: 03 Apr 2024ICLR 2021 PosterReaders: Everyone
Keywords: Object Detection
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.
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One-sentence Summary: We propose Unbiased Teacher to jointly address the pseudo-labeling bias issue and the overfitting issue in semi-supervised object detection, and our model performs favorably against existing works on COCO-standard, COCO-additional, and VOC.
Supplementary Material: zip
Code: [![github](/images/github_icon.svg) facebookresearch/unbiased-teacher](https://github.com/facebookresearch/unbiased-teacher) + [![Papers with Code](/images/pwc_icon.svg) 3 community implementations](https://paperswithcode.com/paper/?openreview=MJIve1zgR_)
Data: [COCO 10% labeled data](https://paperswithcode.com/dataset/coco-10-labeled-data), [MS COCO](https://paperswithcode.com/dataset/coco)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2102.09480/code)
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