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.

翻译：由于地貌图的空间分辨率低,以及边界像素比例极低造成的不平衡问题,预测地貌面罩的界限通常不准确。为了解决这些问题,我们提出了一个概念简单而有效的后处理完善框架,以根据任何实例分解模型(称为BPR)的结果提高边界质量。根据更接近地段边界的构想,我们在预测地段边界一带抽取和完善一系列小边界补丁。通过高分辨率的边界补丁网加以完善。拟议的BPR框架大大改进了市景基准的Mas K R-CNN基线,特别是在边界-水量测量基准方面。此外,通过将BPR框架应用于多式变形+SegFix基线,我们到达了市景板第一处。