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arXiv · 2106.10213

A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation

Feng Luo·Bin-Bin Gao·Jiangpeng Yan·Xiu Li
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Abstract

Boundary-based instance segmentation has drawn much attention since of its attractive efficiency. However, existing methods suffer from the difficulty in long-distance regression. In this paper, we propose a coarse-to-fine module to address the problem. Approximate boundary points are generated at the coarse stage and then features of these points are sampled and fed to a refined regressor for fine prediction. It is end-to-end trainable since differential sampling operation is well supported in the module. Furthermore, we design a holistic boundary-aware branch and introduce instance-agnostic supervision to assist regression. Equipped with ResNet-101, our approach achieves 31.7\% mask AP on COCO dataset with single-scale training and testing, outperforming the baseline 1.3\% mask AP with less than 1\% additional parameters and GFLOPs. Experiments also show that our proposed method achieves competitive performance compared to existing boundary-based methods with a lightweight design and a simple pipeline.

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Feng Luo, Bin-Bin Gao, Jiangpeng Yan, Xiu Li. 2021-06-18. A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation. https://arxiv.org/abs/2106.10213

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