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:
this https URL.

本研究提出一个新的模型 Prompt-Adapter，将预训练的提示调整与高效自适应网络相结合，用于高效的视觉 - 语言模型适应过程，超过公共数据集中少量数据情况下的现有方法，在此基础上，探讨了多任务预训练初始化与 prompt tuning 相结合的思想。