计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (8): 1-15.DOI: 10.3778/j.issn.1002-8331.2308-0271

• 热点与综述 • 上一篇    下一篇

基于深度元学习的小样本图像分类研究综述

周伯俊,陈峙宇   

  1. 1.南通大学 工程训练中心,江苏 南通 226019
    2.河海大学 计算机与信息学院,南京 211100
  • 出版日期:2024-04-15 发布日期:2024-04-15

Survey of Few-Shot Image Classification Based on Deep Meta-Learning

ZHOU Bojun, CHEN Zhiyu   

  1. 1.Engineering Training Center, Nantong University, Nantong, Jiangsu 226019, China
    2.School of Computer and Information, Hohai University, Nanjing 211100, China
  • Online:2024-04-15 Published:2024-04-15

摘要: 深度元学习是解决小样本分类问题的流行范式。对近年来基于深度元学习的小样本图像分类算法进行了详细综述。从问题的描述出发对基于深度元学习的小样本图像分类算法进行概括,并介绍了常用小样本图像分类数据集及评价准则;分别从基于模型的深度元学习方法、基于优化的深度元学习方法以及基于度量的深度元学习方法三个方面对其中的典型模型以及最新研究进展进行详细阐述。最后,给出了现有算法在常用公开数据集上的性能表现,总结了该课题中的研究热点,并讨论了未来的研究方向。

关键词: 深度学习, 元学习, 小样本学习, 图像分类

Abstract: Deep meta-learning has emerged as a popular paradigm for addressing few-shot classification problems. A comprehensive review of recent advancements in few-shot image classification algorithms based on deep meta-learning is provided. Starting from the problem description, the categorizes of the algorithms based on deep meta-learning for few-shot image classification are summarized, and commonly used few-shot image classification datasets and evaluation criteria are introduced. Subsequently, typical models and the latest research progress are elaborated in three aspects: model-based deep meta-learning methods, optimization-based deep meta-learning methods, and metric-based deep meta-learning methods. Finally, the performance analysis of existing algorithms on popular public datasets is presented, the research hotspots in this topic are summarized, and its future research directions are discussed.

Key words: deep learning, meta learning, few-shot learning, image classification