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
this https URL .

提出了一种新的任务设置 —— 统一的语言驱动零样本领域适应 (ULDA)，使得单个模型能够在没有显式域标识知识的情况下适应多样的目标领域。通过在多个视觉层次上对齐模拟特征和目标文本、保留不同区域表示之间的语义相关性以及校正模拟和真实目标视觉特征之间的偏差，该框架在两个场景下实现了竞争性的性能，展示了其优越性和泛化能力。