  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.
