Cross-Modal Data Understanding Advances Through Bukun Ren’s Review of Visual Language Models

A study on visual language models explores how shared semantic frameworks improve image–text understanding across ...
Network World

Forescout brings identity-driven segmentation to multi-vendor networks

The new release of Forescout's 4D platform uses a zone modeling approach to group devices, relying on device identity rather than network location. Network segmentation is a foundational security ...
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 ...
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 ...
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 ...