  Current semantic segmentation models have achieved great success under the
independent and identically distributed (i.i.d.) condition. However, in
real-world applications, test data might come from a different domain than
training data. Therefore, it is important to improve model robustness against
domain differences. This work studies semantic segmentation under the domain
generalization setting, where a model is trained only on the source domain and
tested on the unseen target domain. Existing works show that Vision
Transformers are more robust than CNNs and show that this is related to the
visual grouping property of self-attention. In this work, we propose a novel
hierarchical grouping transformer (HGFormer) to explicitly group pixels to form
part-level masks and then whole-level masks. The masks at different scales aim
to segment out both parts and a whole of classes. HGFormer combines mask
classification results at both scales for class label prediction. We assemble
multiple interesting cross-domain settings by using seven public semantic
segmentation datasets. Experiments show that HGFormer yields more robust
semantic segmentation results than per-pixel classification methods and flat
grouping transformers, and outperforms previous methods significantly. Code
will be available at https://github.com/dingjiansw101/HGFormer.
