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处理复杂背景中视觉目标时的灵活性和优雅性而受到广泛研究。本文提出一种新颖的深度网络架构PolySnake，用于通用轮廓实例分割。受经典Snake算法启发，所提PolySnake通过迭代渐进式轮廓优化策略实现了优异且鲁棒的实例分割性能。在技术层面，PolySnake引入循环更新算子来迭代估计目标轮廓，始终保持对轮廓的单一估计，并使其逐步向目标边界形变。每次迭代中，PolySnake为当前轮廓构建语义丰富的表征，并将其输入循环算子以进行进一步轮廓调整。通过迭代优化，轮廓逐步收敛至紧密包围目标实例的稳定状态。除通用实例分割外，我们还在场景文本检测与车道检测两种特定任务场景中进行了大量实验，以验证PolySnake的有效性与泛化能力。结果表明，所提PolySnake在三个任务的多个主流基准测试中均优于现有先进方法。代码与预训练模型参见https://github.com/fh2019ustc/PolySnake