Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s  

作　　者：Anas W.Abulfaraj 

机构地区：[1]Department of Information Systems,Faculty of Computing and Information Technology,King Abdulaziz University,Rabigh,21911,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第4期1137-1156,共20页计算机、材料和连续体（英文）

基　　金：Institutional Fund Projects under Grant No.(IFPIP:638-830-1443).

摘　　要：The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

关 键 词：Differential equations neural-controlled DE image classification attention maps N-CDE’s 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]