[PolarMask++](TPAMI. 2021）

最新推荐文章于 2021-12-22 11:23:59 发布

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最新推荐文章于 2021-12-22 11:23:59 发布

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分类专栏：笔记

本文链接：https://blog.youkuaiyun.com/weixin_43823854/article/details/117921349

PolarMask++是一种新颖的单次实例分割框架，它使用极坐标系统有效地预测实例掩模和旋转对象。通过极坐标表示，提出极IoU损失和软极中心性，改进了实例中心分类和密集坐标回归。实验证明，该方法在多个挑战性的基准测试中实现了同类最佳的性能，同时保持较低的计算开销。

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1. Contribution

The main contributions of this work are three-fold:

We introduce a new perspective to design a single-shot instance segmentation framework, PolarMask, which predicts instance masks and rotated objects in the polar coordinate in an effective and efficient manner.
With the polar representation, we propose the polar IoU loss and the soft polar centerness for instance center classification and dense coordinate regression.
Rich experiments show that state-of-the-art performances of object instance segmentation and rotated object detection can be achieved with low computational overhead in multiple challenging benchmarks.

2. Method

The heads in PolarMask++ contain three branches, including a classification branch, a polar centerness branch, and a mask regression branch, which predict the class label, the polar centerness score and the length of each polar ray of each pixel respectively, where k and n indicate the number of categories and the number of rays.

2.1 Overview of Mask Segmentation in Polar Coordinate

Polar Representation： n = 36， $θ=10°\theta=10°$
Mass Center
- We find that the mass center is more advantageous than the box center because the mass center has a larger probability of falling inside an instance compared to its box center. Although