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Scale variation across object instances remains a key challenge in objectdetection task. Despite the remarkable progress made by modern detectionmodels, this challenge is particularly evident in the semi-supervised case.While existing semi-supervised object detection methods rely on strictconditions to filter high-quality pseudo labels from network predictions, weobserve that objects with extreme scale tend to have low confidence, resultingin a lack of positive supervision for these objects. In this paper, we proposea novel framework that addresses the scale variation problem by introducing amixed scale teacher to improve pseudo label generation and scale-invariantlearning. Additionally, we propose mining pseudo labels using score promotionof predictions across scales, which benefits from better predictions from mixedscale features. Our extensive experiments on MS COCO and PASCAL VOC benchmarksunder various semi-supervised settings demonstrate that our method achieves newstate-of-the-art performance. The code and models are available at\url{https://github.com/lliuz/MixTeacher}.

MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection