GFT: Graph Feature Tuning for Efficient Point Cloud Analysis

• URL: http://arxiv.org/abs/2511.10799v1
• Date: Thu, 13 Nov 2025 20:57:55 GMT
• Title: GFT: Graph Feature Tuning for Efficient Point Cloud Analysis
• Authors: Manish Dhakal, Venkat R. Dasari, Raj Sunderraman, Yi Ding,
• Abstract summary: Graph Features Tuning (GFT) learns a dynamic graph from initial tokenized inputs of the transformer using a lightweight graph convolution network.<n>Experiments on object classification and segmentation tasks show that GFT operates in the same domain, rivalling existing methods.
• Score: 4.161533496483051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-efficient fine-tuning (PEFT) significantly reduces computational and memory costs by updating only a small subset of the model's parameters, enabling faster adaptation to new tasks with minimal loss in performance. Previous studies have introduced PEFTs tailored for point cloud data, as general approaches are suboptimal. To further reduce the number of trainable parameters, we propose a point-cloud-specific PEFT, termed Graph Features Tuning (GFT), which learns a dynamic graph from initial tokenized inputs of the transformer using a lightweight graph convolution network and passes these graph features to deeper layers via skip connections and efficient cross-attention modules. Extensive experiments on object classification and segmentation tasks show that GFT operates in the same domain, rivalling existing methods, while reducing the trainable parameters. Code is at https://github.com/manishdhakal/GFT.

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