  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.
