  Large pre-trained vision-language (VL) models have shown significant promise
in adapting to various downstream tasks. However, fine-tuning the entire
network is challenging due to the massive number of model parameters. To
address this issue, efficient adaptation methods such as prompt tuning have
been proposed. We explore the idea of prompt tuning with multi-task pre-trained
initialization and find it can significantly improve model performance. Based
on our findings, we introduce a new model, termed Prompt-Adapter, that combines
pre-trained prompt tunning with an efficient adaptation network. Our approach
beat the state-of-the-art methods in few-shot image classification on the
public 11 datasets, especially in settings with limited data instances such as
1 shot, 2 shots, 4 shots, and 8 shots images. Our proposed method demonstrates
the promise of combining prompt tuning and parameter-efficient networks for
efficient vision-language model adaptation. The code is publicly available at:
https://github.com/Jingchensun/prompt_adapter.
