  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 match 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
object localization. Experiments show that the proposed approach achieves
competitive performances on the Cityscapes, KINS, SBD and COCO datasets while
being efficient for real-time applications with a speed of 32.3 fps for
512$\times$512 images on a 1080Ti GPU. The code is available at
https://github.com/zju3dv/snake/.
