While there are a lot of models for instance segmentation, PolarMask stands
out as a unique one that represents an object by a Polar coordinate system.
With an anchor-box-free design and a single-stage framework that conducts
detection and segmentation at one time, PolarMask is proved to be able to
balance efficiency and accuracy. Hence, it can be easily connected with other
downstream real-time applications. In this work, we observe that there are two
deficiencies associated with PolarMask: (i) inability of representing concave
objects and (ii) inefficiency in using ray regression. We propose MP-PolarMask
(Multi-Point PolarMask) by taking advantage of multiple Polar systems. The main
idea is to extend from one main Polar system to four auxiliary Polar systems,
thus capable of representing more complicated convex-and-concave-mixed shapes.
We validate MP-PolarMask on both general objects and food objects of the COCO
dataset, and the results demonstrate significant improvement of 13.69% in AP_L
and 7.23% in AP over PolarMask with 36 rays.

通过扩展多个极坐标系统，提出了 MP-PolarMask（多点极坐标掩膜）模型，用于表示更复杂的凸凹混合形状，在 COCO 数据集的通用对象和食物对象中验证了模型，在 AP_L 上的改进为 13.69%，AP 上的改进为 7.23%。

MP-PolarMask：凹面图像的更快和更详细的实例分割

MP-PolarMask: A Faster and Finer Instance Segmentation for Concave  Images

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.

本文介绍了一种基于 PolarMask 的实例分割方法，它将实例分割问题作为极坐标系下的实例中心分类和密集距离回归，使用了两个有效的方法分别处理高质量实例中心的采样和密集距离回归的优化问题，可以在 COCO 数据集的单模型和单尺度训练 / 测试中取得 32.9% 的掩膜平均精度 (mAP)，是一种更简单、更灵活且表现优异的单次实例分割框架，为单次实例分割任务提供了一个强有力的基础。