LanDA: Language-Guided Multi-Source Domain Adaptation

Understand

Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when transferring knowledge from multiple labeled source domains to an unlabeled target domain.

• However, existing MSDA techniques assume target domain images are available, yet overlook image-rich semantic information.
• Consequently, an open question is whether MSDA can be guided solely by textual cues in the absence of target domain images.
• By employing a multimodal model with a joint image and language embedding space, we propose a novel language-guided MSDA approach, termed LanDA, based on optimal transfer theory, which facilitates the transfer of multiple source domains to a new target domain, requiring only a textual description of the target domain without needing even a single target domain image, while retaining task-relevant information.