IEEE

Improving GNNs for Image Classification: Addressing Homophily Challenges

Abstract: Graph Neural Networks (GNNs) are rapidly becoming essential tools in deep learning, but their effectiveness when applied to images is often limited by challenges in graph representation.
Scientific Research Publishing

A Lightweight MobileViT with a Dual-Path Attention Mechanism for MRI Image Classification ()

Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of ...
IEEE

CNNGP: Multi-tree Genetic Programming Integrating Pre-trained CNN Features for Fine-Grained Image Classification

Abstract: Fine-grained image classification (FGIC) remains a challenging task due to subtle inter-class differences and significant intra-class variations, particularly under limited training data.
IEEE

Bridging CNN and Transformer With Cross-Attention Fusion Network for Hyperspectral Image Classification

Abstract: Feature representation is crucial for hyperspectral image (HSI) classification. However, existing convolutional neural network (CNN)-based methods are limited by the convolution kernel and ...
IEEE

UAV Image Classification: A Comparative Evaluation of CNN Models

Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...
CNN

Fake explosions, fake missiles, fake troops: AI videos and images of Iran war spread widely on social media

After Russia invaded Ukraine in 2022, social media was littered with crude fakes that were presented as fresh images of the war but were either photoshopped phonies or mislabeled clips taken from ...
IEEE

A Lightweight Spatial–Spectral Deformable CNN for UAV Hyperspectral Image Classification

Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
IEEE

A CNN network for coronary chronic total occlusion state classification using image data

Abstract: Chronic total occlusion (CTO) is a critical determinant of treatment efficacy in coronary artery disease, but its accurate diagnosis remains heavily reliant on the expertise of experienced ...