  We present Polite Teacher, a simple yet effective method for the task of
semi-supervised instance segmentation. The proposed architecture relies on the
Teacher-Student mutual learning framework. To filter out noisy pseudo-labels,
we use confidence thresholding for bounding boxes and mask scoring for masks.
The approach has been tested with CenterMask, a single-stage anchor-free
detector. Tested on the COCO 2017 val dataset, our architecture significantly
(approx. +8 pp. in mask AP) outperforms the baseline at different supervision
regimes. To the best of our knowledge, this is one of the first works tackling
the problem of semi-supervised instance segmentation and the first one devoted
to an anchor-free detector.
