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.

翻译：由于在复杂背景中处理视觉物体的灵活性和优雅度,我们积极研究了基于光学的图象分割法。在这项工作中,我们提出一个新的深层次网络结构,即PolySnake,用于基于等离子的图象分割法。在经典的蛇算法的驱动下,拟议的PolySnake以迭接和渐进的等距精细化战略,取得优美和稳健的分割性能。在技术上,PolySnake引入了一个经常性更新操作器,以迭接地估计对象轮廓。它维持着对逐渐向对象边界变形的轮廓的单一估计。在每次迭代,PolySnake为当前等离子构造建立一个内容丰富的语义表达器,并将它提供给经常性操作员,以便进一步调整。通过迭接式精细的精细度算法,该轮廓终于逐渐接近稳定状态,紧紧紧地连接着对象实例。此外,我们通过一个压缩的常规结构设计,确保在多个迭接结构下运行效率。正在进行广泛的实验,以验证我们的方法的优点为基础为基础的方法的优点,在当前的图式Spreal-chausbusmal-commabusmexbusmus