Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Roy, Santanu; Suresh, Ashvath; Adhikari, Prashant; Gupta, Archit Title A name given to the resource Edge Attention Module for Object Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the International Joint Conference on Neural Networks; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IJCNN64981.2025.11227631" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IJCNN64981.2025.11227631</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023977072?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023977072?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roy S., Dept. of Computer Science, NIIT University, Jaipur, India; Suresh A., Dept. of Computer Science and Engg, Christ (Deemed to be University), Bangalore, India; Adhikari P., Dept. of Computer Science, Christ (Deemed to be University), Bangalore, India; Gupta A., Dept. of Computer Science, NIIT University, Jaipur, India Description An account of the resource A novel edge attention-based Convolutional Neural Network (CNN) is proposed in this research for object classification task. With the advent of advanced computing technology, CNN models have achieved to remarkable success, particularly in computer vision applications. Nevertheless, the efficacy of the conventional CNN is often hindered due to class imbalance and inter-class similarity problems, which are particularly prominent in the computer vision field. In this research, we introduce for the first time an Edge Attention Module (EAM) consisting of a Max-Min pooling layer, followed by convolutional layers. This Max-Min pooling is entirely a novel pooling technique, specifically designed to capture only the edge information that is crucial for any object classification task. Therefore, by integrating this novel pooling technique into the attention module, the CNN network inherently prioritizes on essential edge features, thereby boosting the accuracy and F1-score of the model significantly. We have implemented our proposed EAM or 2EAMs on several standard pre-trained CNN models for Caltech-101, Caltech-256, CIFAR-100 and Tiny ImageNet-200 datasets. The extensive experiments reveal that our proposed framework (that is, EAM with CNN and 2EAMs with CNN), outperforms all pre-trained CNN models as well as recent trend models Pooling-based Vision Transformer (PiT), Convolutional Block Attention Module (CBAM), and ConvNext, by substantial margins. We have achieved the accuracy of 95.5% and 86% by the proposed framework on Caltech-101 and Caltech-256 datasets, respectively. So far, this is the best results on these datasets, to the best of our knowledge. All the codes along with graphs, and their classification reports are shared on an anonymous GitHub link: https://anonymous.4open.science/r/Object-Classification-7BE5. 2025 IEEE. Subject The topic of the resource Convolutional Neural Network (CNN); Edge Attention Module (EAM); Max-Min Pooling; Vision Transformer (ViT) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 21614393; ISBN: 979-833151042-8; CODEN: 85OFA Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online