  Achieving robust generalization across diverse data domains remains a
significant challenge in computer vision. This challenge is important in
safety-critical applications, where deep-neural-network-based systems must
perform reliably under various environmental conditions not seen during
training. Our study investigates whether the generalization capabilities of
Vision Foundation Models (VFMs) and Unsupervised Domain Adaptation (UDA)
methods for the semantic segmentation task are complementary. Results show that
combining VFMs with UDA has two main benefits: (a) it allows for better UDA
performance while maintaining the out-of-distribution performance of VFMs, and
(b) it makes certain time-consuming UDA components redundant, thus enabling
significant inference speedups. Specifically, with equivalent model sizes, the
resulting VFM-UDA method achieves an 8.4$\times$ speed increase over the prior
non-VFM state of the art, while also improving performance by +1.2 mIoU in the
UDA setting and by +6.1 mIoU in terms of out-of-distribution generalization.
Moreover, when we use a VFM with 3.6$\times$ more parameters, the VFM-UDA
approach maintains a 3.3$\times$ speed up, while improving the UDA performance
by +3.1 mIoU and the out-of-distribution performance by +10.3 mIoU. These
results underscore the significant benefits of combining VFMs with UDA, setting
new standards and baselines for Unsupervised Domain Adaptation in semantic
segmentation.
