  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. We present extensive experiments across different transfer
scenarios using a suite of relevant benchmarks, demonstrating that LanDA
outperforms standard fine-tuning and ensemble approaches in both target and
source domains.
