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Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
Feb. 27, 2024, 5:47 a.m. | Hongyu Sun, Yongcai Wang, Wang Chen, Haoran Deng, Deying Li
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
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