  The challenge of semantic segmentation in Unsupervised Domain Adaptation
(UDA) emerges not only from domain shifts between source and target images but
also from discrepancies in class taxonomies across domains. Traditional UDA
research assumes consistent taxonomy between the source and target domains,
thereby limiting their ability to recognize and adapt to the taxonomy of the
target domain. This paper introduces a novel approach, Cross-Domain Semantic
Segmentation on Inconsistent Taxonomy using Vision Language Models (CSI), which
effectively performs domain-adaptive semantic segmentation even in situations
of source-target class mismatches. CSI leverages the semantic generalization
potential of Visual Language Models (VLMs) to create synergy with previous UDA
methods. It leverages segment reasoning obtained through traditional UDA
methods, combined with the rich semantic knowledge embedded in VLMs, to relabel
new classes in the target domain. This approach allows for effective adaptation
to extended taxonomies without requiring any ground truth label for the target
domain. Our method has shown to be effective across various benchmarks in
situations of inconsistent taxonomy settings (coarse-to-fine taxonomy and open
taxonomy) and demonstrates consistent synergy effects when integrated with
previous state-of-the-art UDA methods. The implementation is available at
http://github.com/jkee58/CSI.
