  As acquiring pixel-wise annotations of real-world images for semantic
segmentation is a costly process, a model can instead be trained with more
accessible synthetic data and adapted to real images without requiring their
annotations. This process is studied in unsupervised domain adaptation (UDA).
Even though a large number of methods propose new adaptation strategies, they
are mostly based on outdated network architectures. As the influence of recent
network architectures has not been systematically studied, we first benchmark
different network architectures for UDA and newly reveal the potential of
Transformers for UDA semantic segmentation. Based on the findings, we propose a
novel UDA method, DAFormer. The network architecture of DAFormer consists of a
Transformer encoder and a multi-level context-aware feature fusion decoder. It
is enabled by three simple but crucial training strategies to stabilize the
training and to avoid overfitting to the source domain: While (1) Rare Class
Sampling on the source domain improves the quality of the pseudo-labels by
mitigating the confirmation bias of self-training toward common classes, (2) a
Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote
feature transfer from ImageNet pretraining. DAFormer represents a major advance
in UDA. It improves the state of the art by 10.8 mIoU for GTA-to-Cityscapes and
5.4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes
such as train, bus, and truck well. The implementation is available at
https://github.com/lhoyer/DAFormer.
