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More Attentional Local Descriptors for Few-Shot Learning

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Artificial Neural Networks and Machine Learning – ICANN 2020 (ICANN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12396))

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Abstract

Learning from a few examples remains a key challenge for many computer vision tasks. Few-shot learning is proposed to tackle this problem. It aims to learn a classifier to classify images when each class contains only few samples with supervised information in image classification. So far, existing methods have achieved considerable progress, which use fully connected layer or global average pooling as the final classification method. However, due to the lack of samples, global feature may no longer be useful. In contrast, the local feature is more conductive to few-shot learning, but inevitably there will be some noises. In the meanwhile, inspired by human visual systems, the attention mechanism can obtain more valuable information and be widely used in various areas. Therefore, in this paper, we propose a method called More Attentional Deep Nearest Neighbor Neural Network (MADN4 in short) that combines the local descriptors with attention mechanism and is trained end-to-end from scratch. The experimental results on four benchmark datasets demonstrate the superior capability of our method.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 61732011 and Grant 61702358, in part by the Beijing Natural Science Foundation under Grant Z180006, in part by the Key Scientific and Technological Support Project of Tianjin Key Research and Development Program under Grant 18YFZCGX00390, and in part by the Tianjin Science and Technology Plan Project under Grant 19ZXZNGX00050.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Hui Li, Liu Yang & Fei Gao

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  1. Hui Li
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  2. Liu Yang
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  3. Fei Gao
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Correspondence to Liu Yang .

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Editors and Affiliations

  1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  3. Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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Li, H., Yang, L., Gao, F. (2020). More Attentional Local Descriptors for Few-Shot Learning. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-61609-0_33

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