Approximated Prompt Tuning for Vision-Language Pre-trained Models

TL;DR

This paper introduces Approximated Prompt Tuning (APT), a novel method that significantly reduces computational costs in vision-language pre-trained models while maintaining or improving performance on various downstream tasks.

Contribution

The paper reveals that soft prompt tokens can be approximated via information diffusion steps, enabling a more efficient prompt tuning method for Transformer-based VLP models.

Findings

APT achieves +7.01% accuracy on METER

APT reduces computation overhead by 82.30% on METER

APT outperforms conventional prompt tuning methods

Abstract

Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models, and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification and StableDiffusion for text-to-image generation. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +7.01% accuracy and -82.30% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches.