Improving Semantic Segmentation via Decoupled Body and Edge Supervision

@article{Li2020ImprovingSS,
  title={Improving Semantic Segmentation via Decoupled Body and Edge Supervision},
  author={Xiangtai Li and Xia Li and Li Zhang and Guangliang Cheng and Jianping Shi and Zhouchen Lin and Shaohua Tan and Yunhai Tong},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.10035}
}
Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion. In this paper, a new paradigm for semantic segmentation is proposed. Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image. To do so, we… Expand
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