Precise and accurate predictions over boundary areas are essential for semantic segmentation. However, the commonly-used convolutional operators tend to smooth and blur local detail cues, making it difficult for deep models to generate accurate boundary predictions. In this paper, we introduce an operator-level approach to enhance semantic boundary awareness, so as to improve the prediction of the deep semantic segmentation model. Specifically, we first formulate the boundary feature enhancement as an anisotropic diffusion process. We then propose a novel learnable approach called semantic diffusion network (SDN) to approximate the diffusion process, which contains a parameterized semantic difference convolution operator followed by a feature fusion module. Our SDN aims to construct a differentiable mapping from the original feature to the inter-class boundary-enhanced feature. The proposed SDN is an efficient and flexible module that can be easily plugged into existing encoder-decoder segmentation models. Extensive experiments show that our approach can achieve consistent improvements over several typical and state-of-the-art segmentation baseline models on challenging public benchmarks. The code will be released soon.

本文提出了一种运用语义扩散网络（SDN）进行边界特征增强来提高深层语义分割模型预测性能的方法，包括运用各向异性扩散过程进行边界特征增强和引入可学习的SDN，通过提出的SDN，可以构建从原始特征到跨类边界增强特征的可微映射。实验证明，我们的方法在几个典型和最先进的分割基线模型上均能实现一致的改进。

语义扩散网络用于语义分割