  Unsupervised domain adaptation (UDA) has been a potent technique to handle
the lack of annotations in the target domain, particularly in semantic
segmentation task. This study introduces a different UDA scenarios where the
target domain contains unlabeled video frames. Drawing upon recent advancements
of self-supervised learning of the object motion from unlabeled videos with
geometric constraint, we design a \textbf{Mo}tion-guided \textbf{D}omain
\textbf{A}daptive semantic segmentation framework (MoDA). MoDA harnesses the
self-supervised object motion cues to facilitate cross-domain alignment for
segmentation task. First, we present an object discovery module to localize and
segment target moving objects using object motion information. Then, we propose
a semantic mining module that takes the object masks to refine the pseudo
labels in the target domain. Subsequently, these high-quality pseudo labels are
used in the self-training loop to bridge the cross-domain gap. On domain
adaptive video and image segmentation experiments, MoDA shows the effectiveness
utilizing object motion as guidance for domain alignment compared with optical
flow information. Moreover, MoDA exhibits versatility as it can complement
existing state-of-the-art UDA approaches. Code at
https://github.com/feipanir/MoDA.
