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

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Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15481))

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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 https://github.com/ahmadisahar/ACCV_FCIL3D.

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Acknowledgement

This work was supported by the North South University (NSU) Conference Travel and Research Grants (CTRG) 2023–2024 (Grant ID: CTRG-23-SEPS-20).

Author information

Authors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Sahar Ahmadi, Morteza Saberi & Farookh Hussain

  2. Data61, CSIRO, Canberra, Australia

    Ali Cheraghian

  3. Australian National University, Canberra, Australia

    Ali Cheraghian

  4. North South University, Dhaka, Bangladesh

    Md.Towsif Abir & Shafin Rahman

  5. York University, Toronto, Canada

    Hamidreza Dastmalchi

Authors
  1. Sahar Ahmadi
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  2. Ali Cheraghian
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  3. Morteza Saberi
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  4. Md.Towsif Abir
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  6. Farookh Hussain
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  7. Shafin Rahman
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Correspondence to Ali Cheraghian .

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Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Ahmadi, S. et al. (2025). Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-Free Adaptor. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15481. Springer, Singapore. https://doi.org/10.1007/978-981-96-0972-7_11

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  • DOI: https://doi.org/10.1007/978-981-96-0972-7_11

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