无线电工程2024，Vol.54Issue(7)：1668-1675,8.DOI:10.3969/j.issn.1003-3106.2024.07.009
结合图神经网络的小样本图像分类方法
A Graph Neural Network-based Few-shot Image Classification Method
齐露露 1俞卫琴1
作者信息
- 1. 上海工程技术大学数理与统计学院,上海 201600
- 折叠
摘要
Abstract
To address the limitations of existing models in capturing comprehensive and diverse features for few-shot image classification tasks,a Graph-Based Dual-Encoding Prototype Network(GB-ProtoNet)is proposed,which employs a dual-feature extractor architecture that effectively captures and fuses global and local feature information by integrating two distinct neural networks.Specifically,a Residual Neural Network(ResNet)is combined with a graph neural network based on Graph SAmple and aggreGatE(GraphSAGE).The ResNet effectively propagates and preserves information across multiple network layers,enabling the extraction of global feature information.The GraphSAGE,on the other hand,shows good performance in processing graph-structured data by sampling and aggregating information from neighboring nodes to extract local feature information.During training,the GB-ProtoNet utilizes label-smoothing cross-entropy loss function to compute the losses and update the model parameters.Extensive experiments conducted on the miniImageNet and CUB-2002-2011 datasets demonstrate that the GB-ProtoNet achieves superior classification accuracy compared to other state-of-the-art models under both 5-Way 1-Shot and 5-Way 5-Shot settings.关键词
小样本学习/图像分类/图谱卷积网络Key words
few-shot learning/image classification/GraphSAGE分类
信息技术与安全科学引用本文复制引用
齐露露,俞卫琴..结合图神经网络的小样本图像分类方法[J].无线电工程,2024,54(7):1668-1675,8.
