  Pre-trained vision-language models (VLMs) have shown impressive performance
on various downstream tasks by utilizing knowledge learned from large data. In
general, the performance of VLMs on target tasks can be further improved by
prompt tuning, which adds context to the input image or text. By leveraging
data from target tasks, various prompt-tuning methods have been studied in the
literature. A key to prompt tuning is the feature space alignment between two
modalities via learnable vectors with model parameters fixed. We observed that
the alignment becomes more effective when embeddings of each modality are
`well-arranged' in the latent space. Inspired by this observation, we proposed
distribution-aware prompt tuning (DAPT) for vision-language models, which is
simple yet effective. Specifically, the prompts are learned by maximizing
inter-dispersion, the distance between classes, as well as minimizing the
intra-dispersion measured by the distance between embeddings from the same
class. Our extensive experiments on 11 benchmark datasets demonstrate that our
method significantly improves generalizability. The code is available at
https://github.com/mlvlab/DAPT.
