IEEE

TGMN: Two-Stage Graph Convolutional Mamba Network for Hyperspectral Image Classification

Abstract: Local spectral features and global spatial context are essential for hyperspectral image (HSI) classification. However, existing methods based on convolutional neural networks (CNNs), graph ...
C&EN

A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules

School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K. Kuano, Hauxton House, Mill Scitech Park, Mill Lane, Cambridge, England CB22 5HX, U.K. Department ...
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 ...
Frontiers

HHBSNet: a global channel–spatial attention and multi-scale dilated convolution network for automatic melasma segmentation

Objective: Melasma is a common acquired facial hyperpigmentation disorder characterized by symmetrical brown patches, often occurring in the zygomatic region, forehead, and upper lip. Its blurred ...
IEEE

BGC-Net: Bilateral Graph Convolutional Network for Weakly-supervised Semantic Segmentation of Large-scale Point Clouds

Abstract: Weakly-supervised point cloud semantic segmentation (WS-PCS) has attracted increasing attention due to the challenge of sparse annotations. A central problem is how to effectively extract ...
Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...