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