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.

本文研究半监督物体检测(SS-OD)中的伪标签偏差问题并引入一种名为“Unbiased Teacher”的方法，通过对过度自信的伪标签进行分类均衡损失，共同训练一个学生和逐渐进步的老师，最终在COCO标准、COCO附加和VOC数据集上使现有的最先进方法显著提高6.8绝对mAP，当只使用0.5、1、2％的标记数据时，相对于有监督的基线，实现了约10 mAP的提高。

无偏半监督目标检测教师