  Robust semantic segmentation under adverse conditions is crucial in
real-world applications. To address this challenging task in practical
scenarios where labeled normal condition images are not accessible in training,
we propose FREST, a novel feature restoration framework for source-free domain
adaptation (SFDA) of semantic segmentation to adverse conditions. FREST
alternates two steps: (1) learning the condition embedding space that only
separates the condition information from the features and (2) restoring
features of adverse condition images on the learned condition embedding space.
By alternating these two steps, FREST gradually restores features where the
effect of adverse conditions is reduced. FREST achieved a state of the art on
two public benchmarks (i.e., ACDC and RobotCar) for SFDA to adverse conditions.
Moreover, it shows superior generalization ability on unseen datasets.
