- A Dual|Branch Fusion of a Graph Convolutional Network and ...🔍
- A Dual|Branch Fusion of a Graph Convolutional Network ...🔍
- Dual|Branch Fusion of Convolutional Neural Network and Graph ...🔍
- A double|branch graph convolutional network based on individual ...🔍
- Dual|Graph Convolutional Network and Dual|View Fusion for Group ...🔍
- A Multibranch Attention Framework by Combining Graph ...🔍
- Two|Branch Deeper Graph Convolutional Network for Hyperspectral ...🔍
- A Dual|channel Progressive Graph Convolutional Network via ...🔍
A Dual|Branch Fusion of a Graph Convolutional Network and ...
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
Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC).
(PDF) A Dual-Branch Fusion of a Graph Convolutional Network and ...
PDF | Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC).
A Dual-Branch Fusion of a Graph Convolutional Network and ... - MDPI
A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification. Sensors 2024, 24(14), 4760 ...
Dual-Branch Fusion of Convolutional Neural Network and Graph ...
Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of ...
(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 abilities in ...
(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 ...
A double-branch graph convolutional network based on individual ...
To implement this operation, we propose a Double-branch Graph Convolutional Attention Neural Network (DGCAN), which uses a graph neural network to filter ...
Dual-Graph Convolutional Network and Dual-View Fusion for Group ...
On this basis, we propose a dual-graph convolution network that uses hypergraphs and graphs to extract users' collaborative information within ...
A Multibranch Attention Framework by Combining Graph ...
Multibranch Fusion: A Multibranch Attention Framework by Combining Graph Convolutional Network and CNN for Hyperspectral Image Classification.
Two-Branch Deeper Graph Convolutional Network for Hyperspectral ...
However, a GCN model usually suffers from the oversmoothing problem (i.e., all nodes' representations converge to a stationary point) when the ...
A Dual-channel Progressive Graph Convolutional Network via ...
As an important branch of GNNs, a Graph Convolutional Network (GCN) has ... The DPGCN includes progressive subgraph sampling and a dual channel fusion module.
Dual Scene Graph Convolutional Network for Motivation Prediction
The two branches (a visual branch and a semantic branch) of the dual-SGCN use input graphs with the same structure, where nodes and edges correspond to objects ...
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-graph convolutional network based on band attention and ...
The network consists of two branches. In the attention branch, band-based dual graphs are constructed to encode the contextual correlation ...
A graph convolutional network with dynamic weight fusion of multi ...
To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose ...
Dual flow fusion graph convolutional network for traffic flow prediction
In this paper, we propose a novel Dual Flow Fusion Graph Convolutional Network (DFFGCN) to solve this problem.
A Dual-Modality Complex-Valued Fusion Method for Predicting Side ...
A Dual-Modality Complex-Valued Fusion Method for Predicting Side Effects of Drug-Drug Interactions Based on Graph Neural Network. IEEE J Biomed Health Inform ...
Full article: Dual convolutional network based on hypergraph and ...
... branch and CNN branch) and a bimodal feature fusion module (BFFM). In the ... Overall structure of the proposed dual convolutional network based on hypergraph and ...
Relational Fusion Networks: Graph Convolutional Networks for ...
However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN ...