  In this paper, we introduce a method to tackle Domain Generalized Semantic
Segmentation (DGSS) by utilizing domain-invariant semantic knowledge from text
embeddings of vision-language models. We employ the text embeddings as object
queries within a transformer-based segmentation framework (textual object
queries). These queries are regarded as a domain-invariant basis for pixel
grouping in DGSS. To leverage the power of textual object queries, we introduce
a novel framework named the textual query-driven mask transformer (tqdm). Our
tqdm aims to (1) generate textual object queries that maximally encode
domain-invariant semantics and (2) enhance the semantic clarity of dense visual
features. Additionally, we suggest three regularization losses to improve the
efficacy of tqdm by aligning between visual and textual features. By utilizing
our method, the model can comprehend inherent semantic information for classes
of interest, enabling it to generalize to extreme domains (e.g., sketch style).
Our tqdm achieves 68.9 mIoU on GTA5$\rightarrow$Cityscapes, outperforming the
prior state-of-the-art method by 2.5 mIoU. The project page is available at
https://byeonghyunpak.github.io/tqdm.
