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 ...
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 ...
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 ...
GitHub

[ICLR 2026] VeloxSeg: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Overview of VeloxSeg. VeloxSeg employs an encoder-decoder architecture with Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC) on the left, using 1x1 convolution as ...
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

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 ...
IEEE

Multiscale Segmentation-Guided Fusion Network for Hyperspectral Image Classification

Abstract: Convolution Neural Networks (CNNs) have demonstrated strong feature extraction capabilities in Euclidean spaces, achieving remarkable success in hyperspectral image (HSI) classification ...