  Unsupervised domain adaptation (UDA) aims to adapt a model trained on the
source domain (e.g. synthetic data) to the target domain (e.g. real-world data)
without requiring further annotations on the target domain. This work focuses
on UDA for semantic segmentation as real-world pixel-wise annotations are
particularly expensive to acquire. As UDA methods for semantic segmentation are
usually GPU memory intensive, most previous methods operate only on downscaled
images. We question this design as low-resolution predictions often fail to
preserve fine details. The alternative of training with random crops of
high-resolution images alleviates this problem but falls short in capturing
long-range, domain-robust context information. Therefore, we propose HRDA, a
multi-resolution training approach for UDA, 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, while maintaining a manageable GPU memory footprint. HRDA
enables adapting small objects and preserving fine segmentation details. It
significantly improves the state-of-the-art performance by 5.5 mIoU for
GTA-to-Cityscapes and 4.9 mIoU for Synthia-to-Cityscapes, resulting in
unprecedented 73.8 and 65.8 mIoU, respectively. The implementation is available
at https://github.com/lhoyer/HRDA.
