Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor

• URL: http://arxiv.org/abs/2410.09237v1
• Date: Fri, 11 Oct 2024 20:23:00 GMT
• Title: Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
• Authors: Sahar Ahmadi, Ali Cheraghian, Morteza Saberi, Md. Towsif Abir, Hamidreza Dastmalchi, Farookh Hussain, Shafin Rahman,
• Abstract summary: This paper introduces a new method to tackle the Few-Shot Continual Incremental Learning problem in 3D point cloud environments. We leverage a foundational 3D model trained extensively on point cloud data. Our approach uses a dual cache system: first, it uses previous test samples based on how confident the model was in its predictions to prevent forgetting, and second, it includes a small number of new task samples to prevent overfitting.
• Score: 9.54964908165465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a new method to tackle the Few-Shot Continual Incremental Learning (FSCIL) problem in 3D point cloud environments. We leverage a foundational 3D model trained extensively on point cloud data. Drawing from recent improvements in foundation models, known for their ability to work well across different tasks, we propose a novel strategy that does not require additional training to adapt to new tasks. Our approach uses a dual cache system: first, it uses previous test samples based on how confident the model was in its predictions to prevent forgetting, and second, it includes a small number of new task samples to prevent overfitting. This dynamic adaptation ensures strong performance across different learning tasks without needing lots of fine-tuning. We tested our approach on datasets like ModelNet, ShapeNet, ScanObjectNN, and CO3D, showing that it outperforms other FSCIL methods and demonstrating its effectiveness and versatility. The code is available at \url{https://github.com/ahmadisahar/ACCV_FCIL3D}.

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