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基于任务感知关系网络的少样本图像分类

郭礼华 王广飞

郭礼华, 王广飞. 基于任务感知关系网络的少样本图像分类[J]. 电子与信息学报, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162
引用本文: 郭礼华, 王广飞. 基于任务感知关系网络的少样本图像分类[J]. 电子与信息学报, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162
GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162
Citation: GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162

基于任务感知关系网络的少样本图像分类

doi: 10.11999/JEIT230162
基金项目: 广东省基础与应用基础研究基金(2022A1515011549, 2023A1515011104)
详细信息
    作者简介:

    郭礼华:男,副教授,研究方向为图像识别、机器学习和图像质量评价

    王广飞:男,硕士生,研究方向为少样本图像分类

    通讯作者:

    郭礼华 guolihua@scut.edu.cn

  • 11 https://plato.stanford.edu/entries/perceptual-learning/
  • 中图分类号: TN911.73; TP391.41

Few-shot Image Classification Based on Task-Aware Relation Network

Funds: Guangdong Basic and Applied Basic Research Foundation (2022A1515011549, 2023A1515011104)
  • 摘要: 针对关系网络(RN)模型缺乏对分类任务整体相关信息的感知能力的问题,该文提出基于任务感知关系网络(TARN)的小样本学习(FSL)算法。引入模糊C均值(FCM)聚类生成基于任务全局分布的类别原型,同时设计任务相关注意力机制(TCA),改进RN中的1对1度量方式,使得在与类别原型对比时,局部特征聚合了任务全局信息。和RN比,在数据集Mini-ImageNet上,5-way 1-shot和5-way 5-shot设置中的分类准确率分别提高了8.15%和7.0%,在数据集Tiered-ImageNet上,5-way 1-shot和5-way 5-shot设置中的分类准确率分别提高了7.81%和6.7%。与位置感知的关系网络模型比,在数据集Mini-ImageNet上,5-way 1-shot设置中分类准确率也提高了1.24%。与其他小样本图像分类算法性能比较,TARN模型在两个数据集上都获得了最佳的识别精度。该方法将任务相关信息和度量网络模型进行结合可以有效提高小样本图像分类准确率。
  • 图  1  TARN模型整体框架

    图  2  FCM模块计算任务相关类别原型的示意图

    图  3  TCA算法流程

    图  4  TARN与RN, PARN的对照Grad-CAM热力图

    图  5  5-way 1-shot模式下,查询样本和类别原型经过t-SNE降维后可视化图

    表  1  Mini-ImageNet数据集上小样本分类准确率(%)

    模型特征提取网络5-way 1-shot5-way 5-shot
    ReptileConv449.9765.99
    RN50.4465.32
    BOIL49.6166.45
    SNAL55.7168.88
    OVE50.0264.58
    FEAT55.1571.61
    PARN55.2271.55
    TARN(本文)56.4671.77
    FEATResNet1262.9678.49
    RN56.6773.73
    TADAM58.5076.70
    DSN64.6079.51
    NCA62.5578.27
    Meta-Baseline63.1779.26
    PSST64.0580.24
    P-Transfer64.2180.38
    TARN(本文)64.8280.73
    下载: 导出CSV

    表  2  Tiered-ImageNet数据集上小样本分类准确率(%)

    模型特征提取网络5-way1-shot5-way 5-shot
    ProtoNetsConv453.3172.69
    RN53.1869.65
    BOIL49.3569.37
    MELR56.373.22
    TARN(本文)57.9574.68
    FEATResNet1270.8084.79
    DSN66.2282.79
    RN66.1880.15
    Meta-Baseline68.6283.74
    NCA68.3583.20
    UniSiam67.0184.47
    MCL72.0186.02
    BaseTransformer72.4684.96
    MELR72.1487.01
    TARN(本文)73.9986.85
    下载: 导出CSV

    表  3  3种模型的训练时间和测试时间对比

    模型特征提取网络训练时间(min)测试时间(ms)
    RN
    Conv4
    215.244.95
    PARN250.263.57
    TARN251.163.93
    RN
    ResNet12
    485.8150.85
    PARN857.9280.57
    TARN861.3281.71
    下载: 导出CSV

    表  4  Mini-ImageNet数据集的消融实验(%)

    RNHCMFCMTCA5-way 1-shot5-way 5-shot
    51.2165.97
    52.3967.12
    54.0067.90
    55.2270.62
    56.4671.77
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-03-16
  • 修回日期:  2023-08-17
  • 网络出版日期:  2023-08-21
  • 刊出日期:  2024-03-27

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