  Visual prompt tuning (VPT) is a promising solution incorporating learnable
prompt tokens to customize pre-trained models for downstream tasks. However,
VPT and its variants often encounter challenges like prompt initialization,
prompt length, and subpar performance in self-supervised pretraining, hindering
successful contextual adaptation. This study commences by exploring the
correlation evolvement between prompts and patch tokens during proficient
training. Inspired by the observation that the prompt tokens tend to share high
mutual information with patch tokens, we propose initializing prompts with
downstream token prototypes. The strategic initialization, a stand-in for the
previous initialization, substantially improves performance in fine-tuning. To
refine further, we optimize token construction with a streamlined pipeline that
maintains excellent performance with almost no increase in computational
expenses compared to VPT. Exhaustive experiments show our proposed approach
outperforms existing methods by a remarkable margin. For instance, it surpasses
full fine-tuning in 19 out of 24 tasks, using less than 0.4% of learnable
parameters on the FGVC and VTAB-1K benchmarks. Notably, our method
significantly advances the adaptation for self-supervised pretraining,
achieving impressive task performance gains of at least 10% to 30%. Besides,
the experimental results demonstrate the proposed SPT is robust to prompt
lengths and scales well with model capacity and training data size. We finally
provide an insightful exploration into the amount of target data facilitating
the adaptation of pre-trained models to downstream tasks. The code is available
at https://github.com/WangYZ1608/Self-Prompt-Tuning.
