  Domain generalized semantic segmentation is an essential computer vision
task, for which models only leverage source data to learn the capability of
generalized semantic segmentation towards the unseen target domains. Previous
works typically address this challenge by global style randomization or feature
regularization. In this paper, we argue that given the observation that
different local semantic regions perform different visual characteristics from
the source domain to the target domain, methods focusing on global operations
are hard to capture such regional discrepancies, thus failing to construct
domain-invariant representations with the consistency from local to global
level. Therefore, we propose the Semantic-Rearrangement-based Multi-Level
Alignment (SRMA) to overcome this problem. SRMA first incorporates a Semantic
Rearrangement Module (SRM), which conducts semantic region randomization to
enhance the diversity of the source domain sufficiently. A Multi-Level
Alignment module (MLA) is subsequently proposed with the help of such diversity
to establish the global-regional-local consistent domain-invariant
representations. By aligning features across randomized samples with
domain-neutral knowledge at multiple levels, SRMA provides a more robust way to
handle the source-target domain gap. Extensive experiments demonstrate the
superiority of SRMA over the current state-of-the-art works on various
benchmarks.
