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Self-attention network for few-shot learning based on nearest-neighbor algorithm

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A Correction to this article was published on 14 March 2023

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Abstract

Few-shot learning is a challenging task because it focuses on classifying new object categories given only limited labeled samples and often results in poor generalization. Most of existing methods based on metric learning are not very simple for the networks. Moreover, these methods cannot solve low-data problem and are unable to extract discriminative features. This paper presents a simple, effective and general framework for few-shot image classification. Specifically, we first exploit data augmentation technique to alleviate overfitting problem. We propose a self-attention position attention module (PAM) which is utilized to extract discriminative features for constructing a few-shot representation model. Furthermore, we design a novel nearest-neighbor learner with feature transformation to obtain the appealing accuracy in few-shot learning (FSL). Our network is comprised of backbone and attention module and trained from scratch in an end-to-end manner. The backbone module is to extract multi-level features. The self-attention PAM is used to discover non-local information and allow long-range dependency. Excellent performance on benchmark demonstrates that our work provides a unified and effective approach for few-shot image classification.

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Funding

This work was supported by the National Science Foundation of Shanghai (No.22ZR1443700).

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  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Guangpeng Wang & Yongxiong Wang

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  1. Guangpeng Wang
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Wang, G., Wang, Y. Self-attention network for few-shot learning based on nearest-neighbor algorithm. Machine Vision and Applications 34, 28 (2023). https://doi.org/10.1007/s00138-023-01375-5

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