Hierarchical Prompt Injector for Domain Generalization Segmentation
Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains. In contrast, structural attributes exhibit cross-domain stability, motivating the use of structural priors for DGSS. Existing methods use prompt learning to transfer such priors into DGSS models, but typically encode each class as a single holistic prompt. Moreover, these methods apply prompts uniformly to all pixels, offering no mechanism to adapt when only a subset of object regions is visible due to viewpoint changes, occlusion, and environmental variation. We address this with \textbf{Spatial Hierarchical Prompts (SHP)} that enrich...
| Authors | Xin Kun Lin, Ruoyu Guo, Jiaqi Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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