中国科学院大学学报2025，Vol.42Issue(6)：806-813,8.DOI:10.7523/j.ucas.2024.008
用于细粒度图像分类的层级注意力双重网络
Dual networks with hierarchical attention for fine-grained image classification
摘要
Abstract
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.关键词
双重网络/细粒度图像分类/层级注意力特征Key words
dual network(DNet)/fine-grained image classification/hierarchical attention features分类
信息技术与安全科学引用本文复制引用
杨涛,王改华..用于细粒度图像分类的层级注意力双重网络[J].中国科学院大学学报,2025,42(6):806-813,8.基金项目
Supported by the National Natural Science Foundation of China(61601176) （61601176）
