  Tremendous efforts have been made on instance segmentation but the mask
quality is still not satisfactory. The boundaries of predicted instance masks
are usually imprecise due to the low spatial resolution of feature maps and the
imbalance problem caused by the extremely low proportion of boundary pixels. To
address these issues, we propose a conceptually simple yet effective
post-processing refinement framework to improve the boundary quality based on
the results of any instance segmentation model, termed BPR. Following the idea
of looking closer to segment boundaries better, we extract and refine a series
of small boundary patches along the predicted instance boundaries. The
refinement is accomplished by a boundary patch refinement network at higher
resolution. The proposed BPR framework yields significant improvements over the
Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware
metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix
baseline, we reached 1st place on the Cityscapes leaderboard.
