  Deep Neural Networks (DNNs)-based semantic segmentation models trained on a
source domain often struggle to generalize to unseen target domains, i.e., a
domain gap problem. Texture often contributes to the domain gap, making DNNs
vulnerable to domain shift because they are prone to be texture-biased.
Existing Domain Generalized Semantic Segmentation (DGSS) methods have
alleviated the domain gap problem by guiding models to prioritize shape over
texture. On the other hand, shape and texture are two prominent and
complementary cues in semantic segmentation. This paper argues that leveraging
texture is crucial for improving performance in DGSS. Specifically, we propose
a novel framework, coined Texture Learning Domain Randomization (TLDR). TLDR
includes two novel losses to effectively enhance texture learning in DGSS: (1)
a texture regularization loss to prevent overfitting to source domain textures
by using texture features from an ImageNet pre-trained model and (2) a texture
generalization loss that utilizes random style images to learn diverse texture
representations in a self-supervised manner. Extensive experimental results
demonstrate the superiority of the proposed TLDR; e.g., TLDR achieves 46.5 mIoU
on GTA-to-Cityscapes using ResNet-50, which improves the prior state-of-the-art
method by 1.9 mIoU. The source code is available at
https://github.com/ssssshwan/TLDR.
