  Video semantic segmentation is a pivotal aspect of video representation
learning. However, significant domain shifts present a challenge in effectively
learning invariant spatio-temporal features across the labeled source domain
and unlabeled target domain for video semantic segmentation. To solve the
challenge, we propose a novel DA-STC method for domain adaptive video semantic
segmentation, which incorporates a bidirectional multi-level spatio-temporal
fusion module and a category-aware spatio-temporal feature alignment module to
facilitate consistent learning for domain-invariant features. Firstly, we
perform bidirectional spatio-temporal fusion at the image sequence level and
shallow feature level, leading to the construction of two fused intermediate
video domains. This prompts the video semantic segmentation model to
consistently learn spatio-temporal features of shared patch sequences which are
influenced by domain-specific contexts, thereby mitigating the feature gap
between the source and target domain. Secondly, we propose a category-aware
feature alignment module to promote the consistency of spatio-temporal
features, facilitating adaptation to the target domain. Specifically, we
adaptively aggregate the domain-specific deep features of each category along
spatio-temporal dimensions, which are further constrained to achieve
cross-domain intra-class feature alignment and inter-class feature separation.
Extensive experiments demonstrate the effectiveness of our method, which
achieves state-of-the-art mIOUs on multiple challenging benchmarks.
Furthermore, we extend the proposed DA-STC to the image domain, where it also
exhibits superior performance for domain adaptive semantic segmentation. The
source code and models will be made available at
\url{https://github.com/ZHE-SAPI/DA-STC}.
