The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https://github.com/zhanggang001/RefineMask.

本文提出了一种名为RefineMask的新方法，通过多阶段方式在实例分割过程中整合细粒度特征，从而使分割精细化，尤其是适用于大对象的分割，并在COCO、LVIS和Cityscapes基准测试中分别取得了2.6、3.4和3.8 AP的显著增益，同时在LVIS测试数据集上超越了2020年的LVIS挑战赛的获胜者并创造了新的最佳成果。

RefineMask：用精细特征实现高质量实例分割