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 generic 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 progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://github.com/fh2019ustc/PolySnake

翻译：基于轮廓的实例分割因其在复杂背景下处理视觉对象的灵活性和优雅性而受到积极研究。本文提出了一种新颖的深度网络架构PolySnake，用于通用基于轮廓的实例分割。受经典Snake算法启发，所提出的PolySnake通过迭代且渐进的轮廓细化策略实现了卓越且鲁棒的分割性能。技术上，PolySnake引入递归更新算子以迭代估计对象轮廓，它维护单个轮廓估计，并逐步将其变形至对象边界。在每次迭代中，PolySnake为当前轮廓构建语义丰富的表示，并将其馈入递归算子以进一步调整轮廓。通过迭代细化，轮廓逐步收敛至紧密包裹对象实例的稳定状态。超越通用实例分割范畴，本文通过大量实验在两个额外特定任务场景（包括场景文本检测和车道检测）中验证了PolySnake的有效性与泛化性。结果表明，所提出的PolySnake在三个任务的多个主流基准上均优于现有先进方法。代码与预训练模型已开源至https://github.com/fh2019ustc/PolySnake。