  This paper introduces Unified Language-driven Zero-shot Domain Adaptation
(ULDA), a novel task setting that enables a single model to adapt to diverse
target domains without explicit domain-ID knowledge. We identify the
constraints in the existing language-driven zero-shot domain adaptation task,
particularly the requirement for domain IDs and domain-specific models, which
may restrict flexibility and scalability. To overcome these issues, we propose
a new framework for ULDA, consisting of Hierarchical Context Alignment (HCA),
Domain Consistent Representation Learning (DCRL), and Text-Driven Rectifier
(TDR). These components work synergistically to align simulated features with
target text across multiple visual levels, retain semantic correlations between
different regional representations, and rectify biases between simulated and
real target visual features, respectively. Our extensive empirical evaluations
demonstrate that this framework achieves competitive performance in both
settings, surpassing even the model that requires domain-ID, showcasing its
superiority and generalization ability. The proposed method is not only
effective but also maintains practicality and efficiency, as it does not
introduce additional computational costs during inference. Our project page is
https://senqiaoyang.com/project/ULDA .
