  Unsupervised domain adaptation (UDA) tries to overcome the tedious work of
labeling data by leveraging a labeled source dataset and transferring its
knowledge to a similar but different target dataset. Meanwhile, current
vision-language models exhibit remarkable zero-shot prediction capabilities. In
this work, we combine knowledge gained through UDA with the inherent knowledge
of vision-language models. We introduce a strong-weak guidance learning scheme
that employs zero-shot predictions to help align the source and target dataset.
For the strong guidance, we expand the source dataset with the most confident
samples of the target dataset. Additionally, we employ a knowledge distillation
loss as weak guidance. The strong guidance uses hard labels but is only applied
to the most confident predictions from the target dataset. Conversely, the weak
guidance is employed to the whole dataset but uses soft labels. The weak
guidance is implemented as a knowledge distillation loss with (shifted)
zero-shot predictions. We show that our method complements and benefits from
prompt adaptation techniques for vision-language models. We conduct experiments
and ablation studies on three benchmarks (OfficeHome, VisDA, and DomainNet),
outperforming state-of-the-art methods. Our ablation studies further
demonstrate the contributions of different components of our algorithm.
