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.

翻译：点云分析通过迁移点云预训练模型已取得了卓越性能。然而，现有模型适配方法通常更新全部模型参数，即全微调范式，其依赖性导致计算成本高昂（如训练GPU内存）且占用大量存储空间，效率低下。本文旨在研究面向点云分析的参数高效迁移学习，在任务性能与参数效率之间实现理想权衡。为实现此目标，我们冻结默认预训练模型的参数，并提出动态适配器：该方法为每个令牌生成动态缩放因子，充分考虑令牌对下游任务的重要性。通过构建内部提示，我们进一步将动态适配器与提示调优无缝集成，捕捉实例特定特征以实现交互。在五个具有挑战性的数据集上进行的广泛实验表明，所提出的DAPT方法在性能上优于全微调对照方法，同时分别将可训练参数和训练GPU内存减少95%和35%。代码已开源：https://github.com/LMD0311/DAPT。