This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Unlike some recent methods that directly regress the coordinates of the object boundary points from an image, deep snake uses a neural network to iteratively deform an initial contour to the object boundary, which implements the classic idea of snake algorithms with a learning-based approach. For structured feature learning on the contour, we propose to use circular convolution in deep snake, which better exploits the cycle-graph structure of a contour compared against generic graph convolution. Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in initial object localization. Experiments show that the proposed approach achieves state-of-the-art performances on the Cityscapes, Kins and Sbd datasets while being efficient for real-time instance segmentation, 32.3 fps for 512$\times$512 images on a 1080Ti GPU. The code will be available at https://github.com/zju3dv/snake/.

本文提出了一种名为deep snake的新型轮廓方法，它使用神经网络迭代地变形初始轮廓以匹配对象边界。作者使用循环卷积来进行轮廓上的结构化特征学习，并且根据deep snake提出了二阶段流程进行实例分割：初始轮廓建议和轮廓变形。 经实验证明，该方法在实时应用中表现出优异的性能。

深度蛇用于实时实例分割