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Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Metrics concerned:Mean Intersection over Union (IoU)
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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