Title: Fine-Grained Prompt-Driven Stylization with Context-Aware Reasoning for Zero-Shot Domain Adaptation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Zero-Shot, Domain Adaptation

Abstract: Zero-shot domain adaptive semantic segmentation (ZSDA) aims to generalize models to unseen target domains without accessing target data during training. Recent methods commonly use vision-language models (VLMs) to simulate target-domain features by guiding stylization with textual prompts. However, these approaches often suffer from two key issues: description mismatch, where generic prompts fail to reflect scene-specific semantics, and prompt-induced discrepancy, where normalization guided by coarse prompts cannot capture spatial variations. Together, these problems lead to a noticeable simulated-vs-real feature gap, reducing adaptation effectiveness. To address this, we propose FineDA, a framework designed to reduce this gap through image-specific prompt reasoning and fine-grained feature stylization. FineDA introduces a scene graph-guided chain- of-thought module that generates contextual, semantically rich target descriptions for each source image. It also incor- porates a prompt-guided local and global stylization module, enabling patch-wise class-specific adaptation while maintaining scene-level consistency. Extensive experiments on standard ZSDA benchmarks and a challenging in-house surgical dataset with adverse visual conditions such as smoke, blood, and low lighting demonstrate the effectiveness and generalization capability of our approach. Code will be released upon publication.