  Point annotations are considerably more time-efficient than bounding box
annotations. However, how to use cheap point annotations to boost the
performance of semi-supervised object detection remains largely unsolved. In
this work, we present Point-Teaching, a weakly semi-supervised object detection
framework to fully exploit the point annotations. Specifically, we propose a
Hungarian-based point matching method to generate pseudo labels for point
annotated images. We further propose multiple instance learning (MIL)
approaches at the level of images and points to supervise the object detector
with point annotations. Finally, we propose a simple-yet-effective data
augmentation, termed point-guided copy-paste, to reduce the impact of the
unmatched points. Experiments demonstrate the effectiveness of our method on a
few datasets and various data regimes.
