计算机工程与应用2024，Vol.60Issue(2)：129-136,8.DOI:10.3778/j.issn.1002-8331.2208-0373
图神经网络的类别解耦小样本分类
Category Decoupled Few-Shot Classification for Graph Neural Network
摘要
Abstract
Existing metric-based few-shot image classification models show some few-shot image learning performance.However,these models often ignore the extraction of key features of the original data being classified,and redundant infor-mation in the image data that is not related to classification is incorporated into the network parameters of the metric method,which easily causes a bottleneck in the performance of few-shot image classification based on metric methods.To address this problem,a category decoupled few-shot image classification model(VT-GNN)based on graph neural network is pro-posed,which combines image self-attention with a variational self-encoder supervised by a classification task as an embed-ding module to obtain information of the original image category decoupled features as a graph node in a graph structure.A set of few-shot training data is constructed as graph structure data by constructing edge features with metric information between nodes through a multilayer perceptron,and few-shot learning is achieved with the help of message passing mech-anism of graph neural network.On the publicly available dataset Mini-Imagenet,VT-GNN achieves 18.10 percentage points and 16.25 percentage points performance gains relative to the baseline graph neural network model in the 5-way 1-shot and 5-way 5-shot settings,respectively.关键词
小样本学习/图神经网络/变分自编码器/图像自注意力Key words
few-shot learning/graph neural network/variational autoencoder/image self-attention分类
信息技术与安全科学引用本文复制引用
邓戈龙,黄国恒,陈紫嫣..图神经网络的类别解耦小样本分类[J].计算机工程与应用,2024,60(2):129-136,8.基金项目
国家自然科学基金广东联合基金(U1701262) （U1701262）
国家自然科学基金(U20A6003). （U20A6003）
