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TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13680))

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Abstract

This paper presents a transformer framework for few-shot learning, termed TransVLAD, with one focus showing the power of locally aggregated descriptors for few-shot learning. Our TransVLAD model is simple: a standard transformer encoder following a NeXtVLAD aggregation module to output the locally aggregated descriptors. In contrast to the prevailing use of CNN as part of the feature extractor, we are the first to prove self-supervised learning like masked autoencoders (MAE) can deal with the overfitting of transformers in few-shot image classification. Besides, few-shot learning can benefit from this general-purpose pre-training. Then, we propose two methods to mitigate few-shot biases, supervision bias and simple-characteristic bias. The first method is introducing masking operation into fine-tuning, by which we accelerate fine-tuning (by more than 3x) and improve accuracy. The second one is adapting focal loss into soft focal loss to focus on hard characteristics learning. Our TransVLAD finally tops 10 benchmarks on five popular few-shot datasets by an average of more than 2%.

H. Li and L. Zhang—made equal contributions to this work

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Acknowledgement

This work is supported in part by National Key Research and Development Program of China (Grant No. 2021YFF1200800), and the Stable Support Plan Program of Shenzhen Natural Science Fund (Grant No. 20200925154942002).

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Authors and Affiliations

  1. Research Institute of Trustworthy Autonomous Systems, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Haoquan Li, Daoan Zhang & Jianguo Zhang

  2. Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Laoming Zhang, Lang Fu & Peng Yang

  3. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

    Peng Yang

  4. Peng Cheng Lab, Shenzhen, China

    Jianguo Zhang

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  1. Haoquan Li
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  6. Jianguo Zhang
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Correspondence to Jianguo Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Li, H., Zhang, L., Zhang, D., Fu, L., Yang, P., Zhang, J. (2022). TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13680. Springer, Cham. https://doi.org/10.1007/978-3-031-20044-1_30

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  • DOI: https://doi.org/10.1007/978-3-031-20044-1_30

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