  Large-scale pre-trained transformers have demonstrated remarkable success in
various computer vision tasks. However, it is still highly challenging to fully
fine-tune these models for downstream tasks due to their high computational and
storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques,
e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have
significantly reduced the computation and storage cost by inserting lightweight
prompt modules into the pre-trained models and tuning these prompt modules with
a small number of trainable parameters, while keeping the transformer backbone
frozen. Although only a few parameters need to be adjusted, most PETuning
methods still require a significant amount of downstream task training data to
achieve good results. The performance is inadequate on low-data regimes,
especially when there are only one or two examples per class. To this end, we
first empirically identify the poor performance is mainly due to the
inappropriate way of initializing prompt modules, which has also been verified
in the pre-trained language models. Next, we propose a Pre-trained Visual
Parameter-efficient (PVP) Tuning framework, which pre-trains the
parameter-efficient tuning modules first and then leverages the pre-trained
modules along with the pre-trained transformer backbone to perform
parameter-efficient tuning on downstream tasks. Experiment results on five
Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that
our proposed method significantly outperforms state-of-the-art PETuning
methods.
