Leveraging Self-Supervised Object Motion for Unsupervised Domain Adaptation in Semantic Segmentation

The core message of this work is that self-supervised object motion information from unlabeled videos can be leveraged as complementary guidance to facilitate cross-domain alignment for semantic segmentation tasks, without requiring any target domain annotations.