  Unsupervised domain adaptation (UDA) and domain generalization (DG) enable
machine learning models trained on a source domain to perform well on unlabeled
or even unseen target domains. As previous UDA&DG semantic segmentation methods
are mostly based on outdated networks, we benchmark more recent architectures,
reveal the potential of Transformers, and design the DAFormer network tailored
for UDA&DG. It is enabled by three training strategies to avoid overfitting to
the source domain: While (1) Rare Class Sampling mitigates the bias toward
common source domain classes, (2) a Thing-Class ImageNet Feature Distance and
(3) a learning rate warmup promote feature transfer from ImageNet pretraining.
As UDA&DG are usually GPU memory intensive, most previous methods downscale or
crop images. However, low-resolution predictions often fail to preserve fine
details while models trained with cropped images fall short in capturing
long-range, domain-robust context information. Therefore, we propose HRDA, a
multi-resolution framework for UDA&DG, that combines the strengths of small
high-resolution crops to preserve fine segmentation details and large
low-resolution crops to capture long-range context dependencies with a learned
scale attention. DAFormer and HRDA significantly improve the state-of-the-art
UDA&DG by more than 10 mIoU on 5 different benchmarks. The implementation is
available at https://github.com/lhoyer/HRDA.
