Tech Xplore

HarmonyGNN boosts graph AI accuracy on four tough benchmarks by up to 9.6%

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...
IEEE

Hyperspectral Image Classification Based on Multiscale Feature Search Graph Convolutional Network With Meta Pseudolabels

Abstract: In recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph ...
Communications of the ACM

Where Are the City Trees? Monitoring Urban Trees across the U.S. Using Generative AI

The use of generative AI enables a novel computational approach to localize individual trees in all cities, despite their ...
Frontiers

DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification

Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

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 ...
SiliconANGLE

Meta’s new image segmentation models can identify objects and people and reconstruct them in 3D

Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...
Scientific Research Publishing

Dual-Dilated Large Kernel Convolution for Visual Attention Network ()

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

A multi-domain graph convolutional network-based prediction model for personalized motor imagery action

Brain-computer interfaces (BCIs) are advanced and innovative systems that enable direct communication between humans and external devices by utilizing data encoded in the brain activity (Shi et al., ...
IEEE

DC-GCN: A Lightweight Graph Convolutional Network Integrating Local and Global Context for Semantic Segmentation

Abstract: Graph convolutional networks (GCNs) have demonstrated remarkable performance in hyperspectral image classification and remote sensing scene recognition. However, their application to ...