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.

Polite Teacher 使用相互学习框架，结合有限的监督信息和无标定实例，通过 confidence thresholding 和 mask scoring 来过滤噪声，进而在 anchor-free detector 上提高了 8% 的 mask AP 值，这是关于半监督实例分割问题的首个 anchor-free 解决方案。

有礼貌的教师：互相学习和伪标签阈值的半监督实例分割

Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding

Letting a deep network be aware of the quality of its own predictions is an
interesting yet important problem. In the task of instance segmentation, the
confidence of instance classification is used as mask quality score in most
instance segmentation frameworks. However, the mask quality, quantified as the
IoU between the instance mask and its ground truth, is usually not well
correlated with classification score. In this paper, we study this problem and
propose Mask Scoring R-CNN which contains a network block to learn the quality
of the predicted instance masks. The proposed network block takes the instance
feature and the corresponding predicted mask together to regress the mask IoU.
The mask scoring strategy calibrates the misalignment between mask quality and
mask score, and improves instance segmentation performance by prioritizing more
accurate mask predictions during COCO AP evaluation. By extensive evaluations
on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain
with different models, and outperforms the state-of-the-art Mask R-CNN. We hope
our simple and effective approach will provide a new direction for improving
instance segmentation. The source code of our method is available at
https://github.com/zjhuang22/maskscoring_rcnn.

本研究旨在解决在实例分割任务中实例分类置信度与实例分割准确度之间不相关的问题，提出了基于遮罩评分的区域卷积神经网络（Mask Scoring R-CNN）方法，该方法可以协调遮罩质量和遮罩分数之间的不一致，从而提高实例分割性能，并在 COCO 数据集上相对于 Mask R-CNN 取得了更好的性能表现。