With the recent development of Semi-Supervised Object Detection (SS-OD)
techniques, object detectors can be improved by using a limited amount of
labeled data and abundant unlabeled data. However, there are still two
challenges that are not addressed: (1) there is no prior SS-OD work on
anchor-free detectors, and (2) prior works are ineffective when pseudo-labeling
bounding box regression. In this paper, we present Unbiased Teacher v2, which
shows the generalization of SS-OD method to anchor-free detectors and also
introduces Listen2Student mechanism for the unsupervised regression loss.
Specifically, we first present a study examining the effectiveness of existing
SS-OD methods on anchor-free detectors and find that they achieve much lower
performance improvements under the semi-supervised setting. We also observe
that box selection with centerness and the localization-based labeling used in
anchor-free detectors cannot work well under the semi-supervised setting. On
the other hand, our Listen2Student mechanism explicitly prevents misleading
pseudo-labels in the training of bounding box regression; we specifically
develop a novel pseudo-labeling selection mechanism based on the Teacher and
Student's relative uncertainties. This idea contributes to favorable
improvement in the regression branch in the semi-supervised setting. Our
method, which works for both anchor-free and anchor-based methods, consistently
performs favorably against the state-of-the-art methods in VOC, COCO-standard,
and COCO-additional.

本文介绍了一种 Unbiased Teacher v2 方法，针对半监督目标检测的主要问题，将 SS-OD 方法推广到 anchor-free 检测器，引入了 Listen2Student 机制，收集学生和教师的相对不确定性，避免学生被错误的伪标签指引。该方法在 VOC、COCO-standard 和 COCO-additional 三个数据集上的表现优于目前的最先进方法。