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

Anas W. Abulfaraj*

Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia

* Corresponding Author: Anas W. Abulfaraj. Email: email

(This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)

Computers, Materials & Continua 2024, 79(1), 1137-1156. https://doi.org/10.32604/cmc.2024.047945

Abstract

The utilization of visual attention enhances the performance of image classification tasks. Previous attention-based 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. N-CDE’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 top N-CDE’s. Additionally, a training methodology is proposed to guarantee that the training problem is sufficiently presented. Two classification tasks including fine-grained visual classification and multi-label classification, are utilized to evaluate the proposed model. The proposed methodology 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.

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Cite This Article

APA Style
Abulfaraj, A.W. (2024). Pervasive attentive neural network for intelligent image classification based on n-cde’s. Computers, Materials & Continua, 79(1), 1137-1156. https://doi.org/10.32604/cmc.2024.047945
Vancouver Style
Abulfaraj AW. Pervasive attentive neural network for intelligent image classification based on n-cde’s. Comput Mater Contin. 2024;79(1):1137-1156 https://doi.org/10.32604/cmc.2024.047945
IEEE Style
A.W. Abulfaraj, "Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s," Comput. Mater. Contin., vol. 79, no. 1, pp. 1137-1156. 2024. https://doi.org/10.32604/cmc.2024.047945



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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