E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

• URL: http://arxiv.org/abs/2203.04074v1
• Date: Tue, 8 Mar 2022 13:36:23 GMT
• Title: E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation
• Authors: Tao Zhang, Shiqing Wei, Shunping Ji
• Abstract summary: We introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. E2EC is efficient for use in real-time applications, with an inference speed of 36 fps for 512*512 images on an NVIDIA A6000 GPU.
• Score: 4.74225248496056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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