Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for 512*512 images on an NVIDIA A6000 GPU. Code will be released at https://github.com/zhang-tao-whu/e2ec.

在本文中，我们提出了一种名为 E2EC 的新型轮廓匹配-based 方法，采用一种可以学习的轮廓初始化架构来取代手工制作的轮廓初始化，以构建更明确的学习目标，提取更好的轮廓特征，同时利用多方位对齐的标签采样方案降低学习难度，提出动态匹配损失函数来动态匹配最合适的预测-基本事实顶点对以提高边界细节质量，结果表明，E2EC 在 KITTI INSTANCE，Semantic Boundaries Dataset，Cityscapes 和 COCO 数据集上可以达到最先进的性能，同时还可以在实时应用中有效运行，对 512*512 图像在 NVIDIA A6000 GPU 上进行推理速度为 36 fps。

E2EC：一种端到端基于轮廓的高质量高速实例分割方法