Text-guided context learning for multi-source domain adaptation

Abstract

Multi-source Unsupervised Domain Adaptation (MUDA) aims to address the domain transfer challenge between multiple labeled source domains and an unlabeled target domain by reducing domain discrepancies. Existing methods typically learn domain-invariant features in a shared feature space between the source and target domains. However, during multi-source distribution alignment, cross-domain distribution adjustment may shift the model’s attention away from category-discriminative regions. Moreover, most approaches focus primarily on global distribution alignment while neglecting the applicability of source domain supervision to the target domain. To address these issues, we propose a multi-source domain adaptation method called Text-guided Context Learning (TCL), which reformulates multi-source transfer as a cross-domain textual context optimization problem within a pretrained vision–language model. Specifically, we first develop a target domain style context-guided feature augmentation strategy. This strategy utilizes textual modal features that incorporate the style of the target domain to optimize the source domain features, improving the applicability of the source domain supervisory information to the target domain while preserving the category-discriminative information of the source domain visual features. Furthermore, TCL proposes active boundary optimization to ensure the class distinguishability of cross-domain context. By designing a boundary loss and a boundary sampling strategy, we guide the model to focus on difficult samples near decision boundaries during learning, ensuring the learned cross-domain context is more sensitive to inter-class differences. Experimental results demonstrate that our method effectively mitigates the shortcomings of prompt-tuning approaches in domain shift tasks and achieves competitive performance across multiple benchmark datasets. Additionally, our method can be effectively generalized to semi-supervised and partial-set domain adaptation tasks, further validating its robustness and versatility.