摘要
针对以往的图像分类方法利用手工提取的特征(或通过神经网络提取的特征)、空间信息关注不足等问题,文章提出一种基于空间注意力的图像分类网络。该网络利用空间注意力模块,对深度网络提取的视觉特征进行空间约束。利用特征的空间信息,使得网络能够对特征在空间上的重要性加以区分,从而使其更具判别性。采用CIFAR-10和CIFAR-100测试集分别进行测试,测试结果表明,该文提出的图像分类网络的图像分类效果明显优于其他深度学习方法。
Aiming at the problems of traditional image classification methods,such as using manually extracted features(or features extracted through neural networks),insufficient attention to spatial information,this paper proposes an image classification network based on spatial attention.The network uses the spatial attention module to spatial constrain on the visual features extracted by the depth network.Using the spatial information of features,the network can distinguish the importance of features in space,thus making them more discriminative.Test with CIFAR-10 and CIFAR-100 test sets respectively,test results show that the proposed image classification network is superior to other depth learning methods in image classification.
作者
徐海燕
郝萍萍
XU Haiyan;HAO Pingping(Shandong Huayu University of Technology,Dezhou 253034,China)
出处
《现代信息科技》
2023年第2期98-100,共3页
Modern Information Technology
基金
2021年山东华宇工学院校级科研项目(2021KJ17)。
关键词
空间注意力
深度学习
计算机视觉
图像分类
spatial attention
deep learning
computer vision
image classification
