A New Method for Efficiently Fine-Tuning 3D Vision Transformers
A novel parameter-efficient fine-tuning (PEFT) algorithm, called Side Token Adaptation on a neighborhood Graph (STAG), has been developed to optimize the fine-tuning of pre-trained 3D point cloud Transformers. This approach addresses the high computational and memory costs associated with existing methods by introducing a lightweight graph convolutional side network that operates in parallel with a frozen backbone model. STAG adapts tokens for downstream tasks through efficient graph convolution and parameter sharing, drastically reducing the number of tunable parameters to just 0.43 million. The method maintains competitive classification accuracy while offering significant reductions in both fine-tuning time and memory consumption, as validated on a new comprehensive benchmark, PCC13. This advancement in efficient fine-tuning represents a key development for applying large, pre-trained transformer models to complex 3D data analysis tasks.
Study Significance: For professionals in natural language processing and machine learning, this research on efficient transformer adaptation offers a directly transferable methodology. The core techniques of token adaptation and leveraging side networks for parameter-efficient fine-tuning can inform strategies for deploying large language models (LLMs) with lower resource overhead. This work provides a concrete framework for achieving robust model performance in specialized domains without the prohibitive cost of full model retraining, a critical consideration for scalable AI applications.
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