Abstract: Vision GNNs (ViGs) divide an image into multiple patches, treating these image patches as graph nodes. The image is represented by extracting explicit features from these patches as node ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...

The human brain is often compared to a computer, but the latest wave of research shows it is closer to a self-building city, ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

ABSTRACT: Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers ...

The prediction of the properties of crystal materials has always been a core issue in materials science and solid-state physics. With the rapid development of computer simulation techniques and ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Correlative imaging is a powerful analytical approach in bioimaging, as it offers ...

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG's ...