计算机与数字工程2024，Vol.52Issue(5)：1373-1376,1405,5.DOI:10.3969/j.issn.1672-9722.2024.05.019
基于协方差度量矩阵的多尺度融合的小样本学习
Few-Shot Learning Based on Multi-Scale Fusion of Covariance Metric Matrix
莫春晗 1陆建峰1
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京 210094
- 折叠
摘要
Abstract
For the image classification problem with limited training samples,the existing deep neural network-based models have exposed many problems.For example,traditional algorithms based on metric learning usually rely on the pre-defined distance measurement,which makes it difficult for the model to capture the differences among classes when there are too many classes to be classified.Moreover,they only focus on the relational calculation of first-order statistics and the utilization of high-level semantic information,and ignore the low-level but abundant visual features.To address above issues,in this paper,a multi-scale fusion al-gorithm is proposed based on the covariance metric matrix.The second-order statistics under different scale is used to update model parameters.Experimental results show that the proposed model can effectively improve the accuracy of few-shot image classifica-tion,which indicates its promising value in real-world applications.关键词
协方差度量矩阵/多尺度融合/小样本学习Key words
covariance measure matrix/multi-scale fusion/few-shot learning分类
信息技术与安全科学引用本文复制引用
莫春晗,陆建峰..基于协方差度量矩阵的多尺度融合的小样本学习[J].计算机与数字工程,2024,52(5):1373-1376,1405,5.
