The excellent performance of graph convolutional networks (GCNs) on non-Euclidean data has drawn widespread attention from the hyperspectral image classification (HSIC) community, where the predefined ...

Abstract: Graph convolutional network (GCN) has gained widespread attention in semisupervised classification tasks. Recent studies show that GCN-based methods have achieved decent performance in ...

Official Pytorch implementation of "Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose", ECCV 2020. rgb-image single-view eccv 3d-mesh 2d-human-pose ...

Attention-Graph-Convolution-Network-for-Image-Segmentation-in-Big-SAR-Imagery-Data this code implements the method proposed in paper "Attention Graph Convolution Network for Image Segmentation in Big ...

The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique for handling intricate signals and classification tasks owing to their end-to-end neural architecture and nonlinear ...

To develop a high-performance deep learning workflow, we use HydraGNN [4], an open source distributed implementation [5] of multi-headed graph convolutional neural networks, along with ADIOS, a ...