IEEE

MCGC-Net: Multi-scale Controllable Graph Convolutional Network on Music Emotion Recognition

Abstract: This paper introduces a novel dynamic graph learning approach for frequency graphs, underpinned by a suite of baseline methodologies and the Multi-scale Controllable Graph Convolutional ...
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 ...
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 ...
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 ...
Oak Ridge National Lab

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...