Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
This work addresses efficiency challenges in 3D point cloud understanding for researchers and practitioners, offering an incremental improvement over existing prompt tuning approaches.
The paper tackles the high computational and storage costs of existing methods for adapting large multi-modal models to 3D point cloud understanding by proposing PPT, a parameter-efficient prompt tuning method that achieves new records on 4 public datasets for tasks like point cloud recognition and part segmentation.
This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on time-consuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method.Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://github.com/auniquesun/PPT.