Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain specific features. However, these approaches depend heavily on the richness of the texture bank, and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose is to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.

翻译：语义分割中的域泛化旨在通过学习域不变特征缓解在未见域上的性能退化。现有方法通过向源域图像添加复杂甚至异常纹理来降低对域特定特征的敏感性，然而这些方法严重依赖纹理库的丰富度，且训练过程耗时。与随意引入纹理或随机增强风格不同，我们聚焦于单一源域本身以实现泛化。本文提出一种新型自适应纹理过滤机制，无需数据增强即可抑制纹理影响，从而消除域特定特征的干扰。进一步，我们设计了配备结构引导增强模块的层级引导泛化网络，其目标是学习域不变泛化知识。通过在广泛使用的数据集上进行大量实验与消融研究，验证了所提模型的有效性，并揭示了其相较于其他最先进方法的优越性。