- Feature Fusion based Parallel Graph Convolutional Neural Network ...🔍
- Word distance assisted dual graph convolutional networks for ...🔍
- A graph convolutional network with dynamic weight fusion of multi ...🔍
- Multigraph Fusion for Dynamic Graph Convolutional Network🔍
- Rain Streak Removal via Dual Graph Convolutional Network🔍
- Merge|and|Split Graph Convolutional Network for Skeleton|Based ...🔍
- Spatial|temporal dual|channel adaptive graph convolutional ...🔍
- Dual convolutional network based on hypergraph and multilevel ...🔍
A Dual|Branch Fusion of a Graph Convolutional Network and ...
Feature Fusion based Parallel Graph Convolutional Neural Network ...
branches, and each branch handles different features. 2.2 ... Deep learning for multilabel remote sensing image annotation with dual-level.
Word distance assisted dual graph convolutional networks for ...
[2], Ziyue Wang, Junjun Guo . Self-adaptive attention fusion for multimodal aspect-based sentiment analysis. Mathematical Biosciences and Engineering, 2024, 21( ...
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 ...
Multigraph Fusion for Dynamic Graph Convolutional Network
This article proposes a novel multigraph fusion method to produce a high-quality graph and a low-dimensional space of original high-dimensional data for the ...
Rain Streak Removal via Dual Graph Convolutional Network
To achieve the image rain removal, we further embed these two graphs and multi-scale dilated convolution into a symmetri- cally skip-connected network ...
Merge-and-Split Graph Convolutional Network for Skeleton-Based ...
Our solution, the Merge-and-Split Graph Convolutional Network, takes a unique perspective, treating interaction recognition as a global problem. It leverages a ...
Spatial-temporal dual-channel adaptive graph convolutional ... - OUCI
Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion ... Authors: Xingwu ...
Dual convolutional network based on hypergraph and multilevel ...
... branch and CNN branch) and a bimodal feature fusion module (BFFM). ... graph reasoning module to achieve the complementary advantages of the dual-branch network.
Dual-Graph Convolutional Network and Dual-View Fusion for Group ...
Group recommendation constitutes a burgeoning research focus in recommendation systems. Despite a multitude of approaches achieving satisfactory outcomes, ...
respectively; (2) a Graph Convolution Networks Module composed of dual-static GCN units and temporal fusion units to obtain the structural semantic features ...
Deep Learning with Graph Convolutional Networks: An Overview ...
The graph convolution network are derived from graph signal processing, and a filter is introduced to define graph convolution, which can be ...
Dual-graph Learning Convolutional Networks for Interpretable ...
Authors. Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, Guorong Wu. Abstract. In this paper, we propose a dual-graph learning convolutional network ...
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's ...
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional ...
In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive ...
Dual-Branch Difference Amplification Graph Convolutional Network ...
Abstract—Hyperspectral image (HSI) change detection aims to identify the differences in multitemporal HSIs. Recently, a graph convolutional network (GCN) ...
Dual Feature Interaction-Based Graph Convolutional Network
Graphs are widely used to model various practical applications. In recent years, graph convolution networks (GCNs) have attracted increasing ...
Dual Graph Convolutional Network for Semantic Segmentation
In contrast to previous work that uses multi-scale feature fusion or dilated convolutions, we propose a novel graph-convolutional network (GCN) ...
Dual graph convolutional network for semantic segmentation - ORA
In contrast to previous work that uses multi-scale feature fusion or dilated convolutions, we propose a novel graph-convolutional network (GCN)
Graph neural network - Wikipedia
A convolutional neural network layer, in the context of computer vision, can be considered a GNN applied to graphs whose nodes are pixels and only adjacent ...
Tony Geng @ University of Rochester
[OSDI 2023] Y.Wang, B.Feng, Z.Wang, T.Geng, A.Li, K.Barker, Y.Ding: "MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication- ...