Results 11 to 20 of about 5,438,478 (196)
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods.
Guocheng Qian +4 more
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DII-GCN: Dropedge Based Deep Graph Convolutional Networks
Symmetry, 2022 Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a significant obstacle for non-grid ...
Jinde Zhu, Guojun Mao, Chunmao Jiang
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An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification
Remote Sensing, 2023 Graph convolutional networks (GCN) have emerged as a powerful alternative tool for analyzing hyperspectral images (HSIs). Despite their impressive performance, current works strive to make GCN more sophisticated through either elaborate architecture or ...
Zijia Zhang +4 more
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Feature recommendation strategy for graph convolutional network [PDF]
Connection Science, 2022 Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes.
Jisheng Qin +3 more
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Epilepsy Detection Based on Graph Convolutional Neural Network and Transformer [PDF]
BIO Web of ConferencesEpilepsy detection is a critical medical task, but traditional methods face challenges in accuracy and reliability due to the difficulty of EEG data acquisition and the limitation of the number of sample seizures. To overcome these challenges, this paper
Nie Shibo
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Human Action Recognition Algorithm Based on Adaptive Shifted Graph Convolutional NeuralNetwork with 3D Skeleton Similarity [PDF]
Jisuanji kexueGraph convolutional neural network(GCN) has achieved good results in the field of human action recognition based on 3D skeleton.However,in most of the existing GCN methods,the construction of the behavior diagram is based on the manual setting of the ...
YAN Wenjie, YIN Yiying
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data
IEEE AccessGraph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures.
Lijun Fan +3 more
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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction [PDF]
, 2021 We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data.
Yejian, Wu +3 more
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E-GCN: graph convolution with estimated labels [PDF]
, 2021 Graph Convolutional Network (GCN) has been commonly applied for semi-supervised learning tasks. How-ever, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi ...
Zeng, Xiaoqin +3 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring.
Kun Chen +5 more
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