A simple scheme to amplify inter-class discrepancy for improving few-shot fine-grained image classification

Author:

Li Xiaoxu,Guo Zijie,Zhu RuiORCID,Ma Zhanyu,Guo Jun,Xue Jing-HaoORCID

Publisher

Elsevier BV

Reference38 articles.

1. Deep metric learning for few-shot image classification: A review of recent developments;Li;Pattern Recognit.,2023

2. Prototypical networks for few-shot learning;Snell,2017

3. SAPENet: Self-attention based prototype enhancement network for few-shot learning;Huang;Pattern Recognit.,2023

4. Matching networks for one shot learning;Vinyals,2016

5. C. Zhang, Y. Cai, G. Lin, C. Shen, DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, 2020, pp. 12200–12210.

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