Language-Guided Attribute Alignment and Semantic Consistency for Zero-Shot Domain Adaptation

Abstract

In cross-domain visual understanding tasks, models often achieve strong performance on the source domain but suffer severe degradation when applied to target domains with substantial distribution shifts. This challenge is particularly prominent under the zero-shot domain adaptation setting, where adaptation must be achieved without access to target-domain samples and instead relies on language guidance to bridge the gap. However, existing approaches typically depend on fixed class names or handcrafted prompt templates, which fail to capture fine-grained semantic attributes present in the target domain. Moreover, the insufficient alignment between visual and linguistic modalities further constrains the transferability of semantic knowledge. To address these issues, we propose an attribute-driven cross-modal feature modulation framework, termed Language-guided Attribute alignment and Semantic Consistency (LASC). On the semantic side, we introduce an attribute-driven prompt generation module that dynamically combines category information with domain-relevant attributes to construct adaptive text prompts, which are aligned with visual features through cross-modal attention for enhanced semantic stability. Furthermore, we incorporate a semantic consistency constraint, where a memory bank enforces intra-class compactness and inter-class separation, ensuring robust discriminability across domains. Extensive experiments demonstrate that our approach achieves significant improvements over state-of-the-art baselines on multiple cross-domain benchmarks, and maintains strong adaptation ability without requiring any target-domain data.