Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud Transformers

• URL: http://arxiv.org/abs/2502.14142v1
• Date: Wed, 19 Feb 2025 22:58:56 GMT
• Title: Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud Transformers
• Authors: Takahiko Furuya,
• Abstract summary: This paper proposes a novel PEFT algorithm for 3D point cloud Transformers, called Side Token Adaptation on a neighborhood Graph (STAG) STAG employs a graph convolutional side network that operates in parallel with a frozen backbone Transformer to adapt tokens to downstream tasks. We present Point Cloud Classification 13 (PCC13), a new benchmark comprising diverse publicly available 3D point cloud datasets.
• Score: 1.19658449368018
• License:
• Abstract: Parameter-efficient fine-tuning (PEFT) of pre-trained 3D point cloud Transformers has emerged as a promising technique for 3D point cloud analysis. While existing PEFT methods attempt to minimize the number of tunable parameters, they still suffer from high temporal and spatial computational costs during fine-tuning. This paper proposes a novel PEFT algorithm for 3D point cloud Transformers, called Side Token Adaptation on a neighborhood Graph (STAG), to achieve superior temporal and spatial efficiency. STAG employs a graph convolutional side network that operates in parallel with a frozen backbone Transformer to adapt tokens to downstream tasks. STAG's side network realizes high efficiency through three key components: connection with the backbone that enables reduced gradient computation, parameter sharing framework, and efficient graph convolution. Furthermore, we present Point Cloud Classification 13 (PCC13), a new benchmark comprising diverse publicly available 3D point cloud datasets, enabling comprehensive evaluation of PEFT methods. Extensive experiments using multiple pre-trained models and PCC13 demonstrates the effectiveness of STAG. Specifically, STAG maintains classification accuracy comparable to existing methods while reducing tunable parameters to only 0.43M and achieving significant reductions in both computational time and memory consumption for fine-tuning. Code and benchmark will be available at: https://github.com/takahikof/STAG

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