  In this paper, we introduce an anchor-box free and single shot instance
segmentation method, which is conceptually simple, fully convolutional and can
be used as a mask prediction module for instance segmentation, by easily
embedding it into most off-the-shelf detection methods. Our method, termed
PolarMask, formulates the instance segmentation problem as instance center
classification and dense distance regression in a polar coordinate. Moreover,
we propose two effective approaches to deal with sampling high-quality center
examples and optimization for dense distance regression, respectively, which
can significantly improve the performance and simplify the training process.
Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with
single-model and single-scale training/testing on challenging COCO dataset. For
the first time, we demonstrate a much simpler and flexible instance
segmentation framework achieving competitive accuracy. We hope that the
proposed PolarMask framework can serve as a fundamental and strong baseline for
single shot instance segmentation tasks. Code is available at:
github.com/xieenze/PolarMask.
