  Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated
remarkable effectiveness in learning generic visual representations. Several
approaches aim to efficiently adapt VLP models to downstream tasks with limited
supervision, aiming to leverage the acquired knowledge from VLP models.
However, these methods suffer from either introducing biased representations or
requiring high computational complexity, which hinders their effectiveness in
fine-tuning the CLIP model. Moreover, when a model is trained on data specific
to a particular domain, its ability to generalize to uncharted domains
diminishes. In this work, we propose Test-Time Distribution LearNing Adapter
(TT-DNA) which directly works during the testing period. Specifically, we
estimate Gaussian distributions to model visual features of the few-shot
support images to capture the knowledge from the support set. The cosine
similarity between query image and the feature distribution of support images
is used as the prediction of visual adapter. Subsequently, the visual adapter's
prediction merges with the original CLIP prediction via a residual connection,
resulting in the final prediction. Our extensive experimental results on visual
reasoning for human object interaction demonstrate that our proposed TT-DNA
outperforms existing state-of-the-art methods by large margins.
