  Domain-generalized urban-scene semantic segmentation (USSS) aims to learn
generalized semantic predictions across diverse urban-scene styles. Unlike
domain gap challenges, USSS is unique in that the semantic categories are often
similar in different urban scenes, while the styles can vary significantly due
to changes in urban landscapes, weather conditions, lighting, and other
factors. Existing approaches typically rely on convolutional neural networks
(CNNs) to learn the content of urban scenes.
  In this paper, we propose a Content-enhanced Mask TransFormer (CMFormer) for
domain-generalized USSS. The main idea is to enhance the focus of the
fundamental component, the mask attention mechanism, in Transformer
segmentation models on content information. To achieve this, we introduce a
novel content-enhanced mask attention mechanism. It learns mask queries from
both the image feature and its down-sampled counterpart, as lower-resolution
image features usually contain more robust content information and are less
sensitive to style variations. These features are fused into a Transformer
decoder and integrated into a multi-resolution content-enhanced mask attention
learning scheme.
  Extensive experiments conducted on various domain-generalized urban-scene
segmentation datasets demonstrate that the proposed CMFormer significantly
outperforms existing CNN-based methods for domain-generalized semantic
segmentation, achieving improvements of up to 14.00\% in terms of mIoU (mean
intersection over union). The source code is publicly available at
\url{https://github.com/BiQiWHU/CMFormer}.
