重庆科技学院学报（自然科学版）2024，Vol.26Issue(1)：41-47,7.DOI:10.19406/j.issn.1673-1980.2024.01.007
基于稀疏注意力关系网络的小样本图像分类方法
Small Sample Image Classification Based on Sparse-Attention Relation Network
摘要
Abstract
To solve the problem of small sample image classification,a Sparse Attention Relationship Network(SARN)model was proposed based on the local connectivity of convolution operations and the attention mechanism on the basic of non-local operations.In the process of non-local operation,the sparse strategy is used to calculate the relevant features involved in the response calculation.The dependence between the relevant features of different spatial locations is established through the sparse attention mechanism,and the connection of semantical irrelevant features is cut off.The subsequent convolution operation suppresses the interference of irrelevant information when performing feature measurement on semantical relevant features of different spacial positions,and improves the overall measurement ability of the model.Through a series of experiments on the Mini-ImageNet and Tiered-Ima-geNet datasets,it is found that SARN achieves significant performance improvement compared with small sample learning model.关键词
小样本学习/度量学习/关系网络/稀疏注意力机制/双重注意力机制Key words
small sample learning/metric learning/relation network/sparse attention mechanism/dual attention mechanism分类
信息技术与安全科学引用本文复制引用
郭礼华,王广飞..基于稀疏注意力关系网络的小样本图像分类方法[J].重庆科技学院学报（自然科学版）,2024,26(1):41-47,7.基金项目
广东省基础与应用基础研究基金项目"基于图神经网络的图像少样本学习算法研究"(2022A1515011549) （2022A1515011549）
