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Improving Few-Shot Image Classification with Self-supervised Learning

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Cloud Computing – CLOUD 2022 (CLOUD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13731))

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Abstract

Few-Shot Image Classification (FSIC) aims to learn an image classifier with only a few training samples. The key challenge of few-shot image classification is to learn this classifier with scarce labeled data. To tackle the issue, we leverage the self-supervised learning (SSL) paradigm to exploit unsupervised information. This work builds upon two-stage training paradigm, to push the current state-of-the-art (SOTA) in solving FSIC problem further. Specifically, we incorporate the traditional self-supervised learning method (TSSL) into the pre-training stage and propose an episodic contrastive loss (CL) as an auxiliary supervision for the meta-training stage. The proposed bipartite method, called FSIC-SSL, can SOTA task accuracies on two mainstream FSIC benchmark datasets. Our code will be available at https://github.com/SethDeng/FSIC_SSL.

S. Deng and D. Liao—Equal contribution.

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Acknowledgment

This work is supported in part by National Key R &D Program of China (No. 2019YFB2102100), Key-Area Research and Development Program of Guangdong Province (No. 2020B010164003), and Shenzhen Science and Technology Innovation Commission (No. JCYJ20190812160003719).

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000, China

    Shisheng Deng, Xitong Gao, Juanjuan Zhao & Kejiang Ye

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Shisheng Deng

  3. University of Macau, Macau SAR, 999078, China

    Dongping Liao

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  1. Shisheng Deng
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Correspondence to Xitong Gao .

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  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China

    Kejiang Ye

  2. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Deng, S., Liao, D., Gao, X., Zhao, J., Ye, K. (2022). Improving Few-Shot Image Classification with Self-supervised Learning. In: Ye, K., Zhang, LJ. (eds) Cloud Computing – CLOUD 2022. CLOUD 2022. Lecture Notes in Computer Science, vol 13731. Springer, Cham. https://doi.org/10.1007/978-3-031-23498-9_5

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