Contour-based instance segmentation has been actively studied, thanks to its
flexibility and elegance in processing visual objects within complex
backgrounds. In this work, we propose a novel deep network architecture, i.e.,
PolySnake, for contour-based instance segmentation. Motivated by the classic
Snake algorithm, the proposed PolySnake achieves superior and robust
segmentation performance with an iterative and progressive contour refinement
strategy. Technically, PolySnake introduces a recurrent update operator to
estimate the object contour iteratively. It maintains a single estimate of the
contour that is progressively deformed toward the object boundary. At each
iteration, PolySnake builds a semantic-rich representation for the current
contour and feeds it to the recurrent operator for further contour adjustment.
Through the iterative refinements, the contour finally progressively converges
to a stable status that tightly encloses the object instance. Moreover, with a
compact design of the recurrent architecture, we ensure the running efficiency
under multiple iterations. Extensive experiments are conducted to validate the
merits of our method, and the results demonstrate that the proposed PolySnake
outperforms the existing contour-based instance segmentation methods on several
prevalent instance segmentation benchmarks. The codes and models are available
at https://github.com/fh2019ustc/PolySnake