  As the scale of vision models continues to grow, the emergence of Visual
Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has
gained attention due to its superior performance compared to traditional
full-finetuning. However, the conditions favoring VPT (the ``when") and the
underlying rationale (the ``why") remain unclear. In this paper, we conduct a
comprehensive analysis across 19 distinct datasets and tasks. To understand the
``when" aspect, we identify the scenarios where VPT proves favorable by two
dimensions: task objectives and data distributions. We find that VPT is
preferrable when there is 1) a substantial disparity between the original and
the downstream task objectives (e.g., transitioning from classification to
counting), or 2) a similarity in data distributions between the two tasks
(e.g., both involve natural images). In exploring the ``why" dimension, our
results indicate VPT's success cannot be attributed solely to overfitting and
optimization considerations. The unique way VPT preserves original features and
adds parameters appears to be a pivotal factor. Our study provides insights
into VPT's mechanisms, and offers guidance for its optimal utilization.
