  Pre-trained language models (PLMs) have played an increasing role in
multimedia research. In terms of vision-language (VL) tasks, they often serve
as a language encoder and still require an additional fusion network for VL
reasoning, resulting in excessive memory overhead. In this paper, we focus on
exploring PLMs as a stand-alone model for VL reasoning tasks. Inspired by the
recently popular prompt tuning, we first prove that the processed visual
features can be also projected onto the semantic space of PLMs and act as
prompt tokens to bridge the gap between single- and multi-modal learning.
However, this solution exhibits obvious redundancy in visual information and
model inference, and the placement of prompt tokens also greatly affects the
final performance. Based on these observations, we further propose a novel
transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP).
Concretely, DVP first deploys a cross-attention module to obtain text-related
and compact visual prompt tokens, thereby greatly reducing the input length of
PLMs. To obtain the optimal placement, we also equip DVP with a
reinforcement-learning based search algorithm, which can automatically merge
DVP with PLMs for different VL tasks via a very short search process. In
addition, we also experiment DVP with the recently popular adapter approach to
keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs
achieve a quick shift between single- and multi-modal tasks. We apply DVP to
two representative PLMs, namely BERT and T5, and conduct extensive experiments
on a set of VL reasoning benchmarks including VQA2.0, GQA and SNLIVE. The
experimental results not only show the advantage of DVP on efficiency and
performance, but also confirm its superiority in adapting pre-trained language
models to VL tasks.
