  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.
