  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.
