  In recent studies on domain adaptation, significant emphasis has been placed
on the advancement of learning shared knowledge from a source domain to a
target domain. Recently, the large vision-language pre-trained model, i.e.,
CLIP has shown strong ability on zero-shot recognition, and parameter efficient
tuning can further improve its performance on specific tasks. This work
demonstrates that a simple domain prior boosts CLIP's zero-shot recognition in
a specific domain. Besides, CLIP's adaptation relies less on source domain data
due to its diverse pre-training dataset. Furthermore, we create a benchmark for
zero-shot adaptation and pseudo-labeling based self-training with CLIP. Last
but not least, we propose to improve the task generalization ability of CLIP
from multiple unlabeled domains, which is a more practical and unique scenario.
We believe our findings motivate a rethinking of domain adaptation benchmarks
and the associated role of related algorithms in the era of CLIP.
