Current instance segmentation methods rely heavily on pixel-level annotated
images. The huge cost to obtain such fully-annotated images restricts the
dataset scale and limits the performance. In this paper, we formally address
semi-supervised instance segmentation, where unlabeled images are employed to
boost the performance. We construct a framework for semi-supervised instance
segmentation by assigning pixel-level pseudo labels. Under this framework, we
point out that noisy boundaries associated with pseudo labels are double-edged.
We propose to exploit and resist them in a unified manner simultaneously: 1) To
combat the negative effects of noisy boundaries, we propose a noise-tolerant
mask head by leveraging low-resolution features. 2) To enhance the positive
impacts, we introduce a boundary-preserving map for learning detailed
information within boundary-relevant regions. We evaluate our approach by
extensive experiments. It behaves extraordinarily, outperforming the supervised
baseline by a large margin, more than 6% on Cityscapes, 7% on COCO and 4.5% on
BDD100k. On Cityscapes, our method achieves comparable performance by utilizing
only 30% labeled images.

本文研究半监督实例分割问题，提出了一种通过引入伪标签的方法，构建包含噪声的像素级标签的半监督实例分割框架，并在框架中同时提出抵制和利用噪声边界的方法，通过大量实验表明该方法的性能优于基线方法。