Intra-inter channel attention for few-shot classification

YANG Liping; ZHANG Tianyang; WANG Yuyang; GU Xiaohua

doi:10.37188/OPE.20233121.3145

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Intra-inter channel attention for few-shot classification

Information Sciences | 更新时间：2023-11-16

- Intra-inter channel attention for few-shot classification
- Optics and Precision Engineering Vol. 31, Issue 21, Pages: 3145-3155(2023)
- 作者机构：
  
  1.重庆大学光电技术及系统教育部重点实验室，重庆 400044
  2.重庆科技学院电气工程学院，重庆 401331
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233121.3145
  CLC： TP391.4
- Received：06 April 2023，
  
  Revised：14 May 2023，
  
  Published：10 November 2023
- 稿件说明：
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杨利平,张天洋,王宇阳等.类内-类间通道注意力少样本分类[J].光学精密工程,2023,31(21):3145-3155.

YANG Liping,ZHANG Tianyang,WANG Yuyang,et al.Intra-inter channel attention for few-shot classification[J].Optics and Precision Engineering,2023,31(21):3145-3155.
杨利平,张天洋,王宇阳等.类内-类间通道注意力少样本分类[J].光学精密工程,2023,31(21):3145-3155. DOI： 10.37188/OPE.20233121.3145.

YANG Liping,ZHANG Tianyang,WANG Yuyang,et al.Intra-inter channel attention for few-shot classification[J].Optics and Precision Engineering,2023,31(21):3145-3155. DOI： 10.37188/OPE.20233121.3145.

摘要

针对元学习少样本分类样本特征鉴别能力不足的问题，提出了一种类内-类间通道注意力少样本分类方法（Intra-inter Channel Attention Few-shot Classification， ICAFSC）。ICAFSC在原型网络基础上设计了一个类内-类间通道注意力模块，该模块通过类内-类间距离度量计算通道权重实现特征加权，提高特征对类别的鉴别能力。为了克服直接在元训练阶段学习类内-类间通道注意力模块容易出现过拟合或欠拟合现象的问题，ICAFSC在原型网络的元训练之前增加一个预训练阶段。该阶段设计具有大量标记样本的分类任务，并利用这些任务充分训练类内-类间通道注意力模块，促使该模块达到较优的状态。在原型网络的元训练和元测试阶段，ICAFSC冻结类内-类间通道注意力模块的参数，分别实现少样本分类经验的学习与迁移。在MiniImagenet数据集上分别开展了1-shot和5-shot的少样本分类实验。实验结果表明：本文提出的类内-类间通道注意力少样本分类方法与原型网络相比，在1-shot和5-shot条件下分类准确率分别提高了1.93%和1.15%。

Abstract

As only one or a few training samples are used for few-shot classification tasks， the features extracted via a prototypical network cannot guarantee much discriminative power. Accordingly， this paper proposes an intra-inter channel attention few-shot classification （ICAFSC） method. This method uses an intra–inter channel attention module （ICAM） to calculate channel weights based on an intra-inter distance metric. The module is integrated into the prototypical network to make the embedded features more discriminative. To overcome the problems of overfitting or underfitting when directly learning the ICAM in the few-shot classification's meta-training stage， ICAFSC adds a pre-training stage before the meta-training of the prototypical network. We design adequate classification tasks with a large number of labeled samples to learn optimal parameters of the ICAM in the pre-training stage. Subsequently， in the meta-training and meta-testing stages of the prototypical network， ICAFSC first freezes the parameters of the ICAM to guarantee a stable channel attention relationship. It then achieves few-shot classification experience learning and transfer via meta-training and meta-testing. We conduct 1-shot and 5-shot few-shot classification experiments on the MiniImagenet dataset. The experimental results indicate that， compared to the prototypical network， the proposed ICAFSC method shows improvements of 1.93% and 1.15% for the 1-shot and 5-shot scenarios， respectively.

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