Dual|Branch Fusion of Convolutional Neural Network and Graph ...

Dual-Branch Fusion of Convolutional Neural Network and Graph ...

The FC layer aims to reshape the output of the last convolution to a one-dimensional vector to be concatenated with miniGCN output. The fused ...

A Dual-Branch Fusion of a Graph Convolutional Network and ... - MDPI

A dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel ...

A Dual-Branch Fusion of a Graph Convolutional Network ... - PubMed

To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses ...

Dual-Branch Fusion of Convolutional Neural Network and Graph ...

Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification. Ali Radman, Masoud Mahdianpari, Brian ...

(PDF) A Dual-Branch Fusion of a Graph Convolutional Network and ...

By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages ...

Dual-Branch Fusion of Convolutional Neural Network and Graph ...

A novel dual-branch fusion of CNN and mini-GCN is proposed in this study for PolSAR image classification. To fully utilize the PolSAR image capacity, different ...

(PDF) Dual-Branch Fusion of Convolutional Neural Network and ...

The convolutional neural networks (CNNs) and graph convolutional networks (GCNs) can drive PolSAR image characteristics by deploying kernel ...

Dual-Branch Fusion of Convolutional Neural Network and ... - Altmetric

Readers on ; Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification · Remote Sensing, December 2022.

Multi-branch fusion graph neural network based on multi-head ...

Therefore, this study introduces a multi-branch graph convolutional model with multi-head attention (MGCNA) for childhood seizure detection. Specifically, the ...

Hybrid graph convolution neural network and branch-and-bound ...

... Convolutional Neural Network (CNN) module with a spatial–temporal fusion graph module. Long-term spatial–temporal disparities. Lu et al. [18] predict ...

Dual-branch convolutional neural network for robust camera model ...

We propose a dual-branch convolutional neural network (CNN) for camera model identification (CMI), where one branch directly uses the three-channel RGB image.

Application of convolutional neural network in fusion and ... - Frontiers

... dual branch CNN network model is 0.100. When the number of ... Dual-graph attention convolution network for 3-d point cloud classification.

Full article: Dual convolutional network based on hypergraph and ...

In the HGNN branch, an algorithm is developed to exploit the shape features of roads and construct hypergraphs on the HRSI. Then, hypergraph neural networks are ...

Dual-channel deep graph convolutional neural networks - PMC - NCBI

However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance ...

Multiscale Attention Fusion Graph Network for Remote Sensing ...

Considering that graph convolutional neural networks (GCNs) have powerful internal relationship learning ... MAFGNet uses a dual graph convolution ...

FGCNSurv: dually fused graph convolutional network for multi-omics ...

Graph convolutional neural network (GCN) could aggregate and exchange information between neighboring nodes (samples) to obtain embedding for nodes of graph ...

Dual Fusion-Propagation Graph Neural Network for Multi-View ...

This work proposes an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and applies it to deep multi-view clustering tasks and ...

Dual Fusion-Propagation Graph Neural Network for Multi-View ...

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or ...

Dynamic graph convolutional network for assembly behavior ...

Graph convolutional neural network is a deep learning method for processing graph data. It can automatically learn node features and the ...

DCG-Net: Enhanced Hyperspectral Image Classification with Dual ...

... Dual-Branch Convolutional Neural Network and Graph Convolutional Neural Network Integration ... Ding, Multi-feature fusion: Graph neural network and CNN ...