Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

• URL: http://arxiv.org/abs/2206.09500v1
• Date: Sun, 19 Jun 2022 22:57:48 GMT
• Title: Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
• Authors: Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira
• Abstract summary: We present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors. We also introduce Listen2Student mechanism for the unsupervised regression loss.
• Score: 35.41491696151547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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