  Point cloud analysis has achieved outstanding performance by transferring
point cloud pre-trained models. However, existing methods for model adaptation
usually update all model parameters, i.e., full fine-tuning paradigm, which is
inefficient as it relies on high computational costs (e.g., training GPU
memory) and massive storage space. In this paper, we aim to study
parameter-efficient transfer learning for point cloud analysis with an ideal
trade-off between task performance and parameter efficiency. To achieve this
goal, we freeze the parameters of the default pre-trained models and then
propose the Dynamic Adapter, which generates a dynamic scale for each token,
considering the token significance to the downstream task. We further
seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing
Internal Prompts, capturing the instance-specific features for interaction.
Extensive experiments conducted on five challenging datasets demonstrate that
the proposed DAPT achieves superior performance compared to the full
fine-tuning counterparts while significantly reducing the trainable parameters
and training GPU memory by 95% and 35%, respectively. Code is available at
https://github.com/LMD0311/DAPT.
