  Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in
adapting an action recognition model, trained on a labelled source dataset, to
an unlabelled target dataset, without accessing the actual source data. The
previous approaches have attempted to address SFVUDA by leveraging
self-supervision (e.g., enforcing temporal consistency) derived from the target
data itself. In this work, we take an orthogonal approach by exploiting
"web-supervision" from Large Language-Vision Models (LLVMs), driven by the
rationale that LLVMs contain a rich world prior surprisingly robust to
domain-shift. We showcase the unreasonable effectiveness of integrating LLVMs
for SFVUDA by devising an intuitive and parameter-efficient method, which we
name Domain Adaptation with Large Language-Vision models (DALL-V), that
distills the world prior and complementary source model information into a
student network tailored for the target. Despite the simplicity, DALL-V
achieves significant improvement over state-of-the-art SFVUDA methods.
