Results 41 to 50 of about 5,438,478 (196)
Predicting traffic propagation flow in urban road network with multi-graph convolutional network
Complex & Intelligent Systems, 2023 Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast ...
Haiqiang Yang, Zihan Li, Yashuai Qi
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recently, graph convolutional network (GCN) has achieved promising results in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate the new node.
Yao Ding +4 more
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A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting
, 2020 Forecasting the traffic flow is a critical issue for researchers and practitioners in the field of transportation. Using the graph convolutional network (GCN) is widespread in traffic flow forecasting. Existing GCN-based methods mostly rely on undirected
Zhaoyang Li +7 more
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, 2021 Graph Convolutional Networks (GCNs) play a vital role in graph learning tasks such as semi-supervised learning. However, a GCN model requires a large amount of labeled data for verification and model selection, and learning on sparse labeled graphs is ...
Zhou, Meng Chu +5 more
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Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
Frontiers in Neurorobotics, 2021 The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered.
Shuhao Shi +5 more
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Gaze Gesture Recognition by Graph Convolutional Networks
Frontiers in Robotics and AI, 2021 Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success
Lei Shi, Cosmin Copot, Steve Vanlanduit
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HD-GCN:A Hybrid Diffusion Graph Convolutional Network [PDF]
, 2023 The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph ...
Li, Kang +4 more
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Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
Connection Science, 2022 Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in ...
Na Hu +4 more
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Frontiers in Human Neuroscience, 2022 Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative ...
Feng Zhao +7 more
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Remote Sensing, 2020 Although the deep semantic segmentation network (DSSN) has been widely used in remote sensing (RS) image semantic segmentation, it still does not fully mind the spatial relationship cues between objects when extracting deep visual features through ...
Song Ouyang, Yansheng Li
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