Search
Search Results
-
Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction
In the current era of mobile edge networks, a significant challenge lies in overcoming the limitations posed by limited edge storage and...
-
An evolving graph convolutional network for dynamic functional brain network
Brain networks have received extensive attention because of its important significance in understanding human brain organization and analyzing...
-
Fake news detection based on dual-channel graph convolutional attention network
Fake news detection has attracted significant attention since the spread of fake news on social media has affected the media’s credibility. Some...
-
Dependency-position relation graph convolutional network with hierarchical attention mechanism for relation extraction
Existing research extensively incorporates syntactic information, especially dependency trees, to enhance the performance of relation extraction...
-
Hierarchical graph learning with convolutional network for brain disease prediction
In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However,...
-
A cascaded graph convolutional network for point cloud completion
Point cloud completion represents a complex task that entails predicting the complete geometry of a 3D shape from a set representation of the partial...
-
Adaptive shift graph convolutional neural network for hand gesture recognition based on 3D skeletal similarity
Graph convolutional neural networks (GCNs) have shown promising results in the field of hand gesture recognition based on 3D skeletal data. However,...
-
DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network
A traffic network possesses all the basic characteristics of networks, as well as its own distinct features, which have research significance. In...
-
Probabilistic spatio-temporal graph convolutional network for traffic forecasting
Forecasting traffic flow is crucial for Intelligent Traffic Systems (ITS), traffic control, and traffic management systems. Complex spatial and...
-
Thermal infrared action recognition with two-stream shift Graph Convolutional Network
The extensive deployment of camera-based IoT devices in our society is heightening the vulnerability of citizens’ sensitive information and...
-
Graph convolutional dynamic recurrent network with attention for traffic forecasting
Traffic forecasting is a typical spatio-temporal graph modeling problem, which has become one of the key technical issues in modern intelligent...
-
Spatial adaptive graph convolutional network for skeleton-based action recognition
In recent years, great achievements have been made in graph convolutional network (GCN) for non-Euclidean spatial data feature extraction, especially...
-
Spatial dynamic graph convolutional network for traffic flow forecasting
The complex traffic network spatial correlation and the characteristic of high nonlinear and dynamic traffic conditions in the time are the...
-
Co-attention graph convolutional network for visual question answering
Visual Question Answering (VQA) is a challenging task that requires a fine-grained understanding of both the visual content of images and the textual...
-
Multi-stream ternary enhanced graph convolutional network for skeleton-based action recognition
A novel mechanism for skeleton-based action recognition is proposed in this paper by enhancing and fusing diverse skeleton features from distinct...
-
Relation-consistency graph convolutional network for image super-resolution
Convolutional neural networks (CNNs) have been widely exploited in single image super-resolution (SISR) due to their powerful feature representation...
-
Multi-label semantic sharing based on graph convolutional network for image-to-text retrieval
Cross-modal hashing has attracted widespread attention due to its ability to reduce the complexity of storage and retrieval. However, many existing...
-
Adaptive self-propagation graph convolutional network for recommendation
Graph Convolutional Networks (GCNs) have received a lot of attention in recommender systems due to their powerful representation learning ability on...
-
STTD: spatial-temporal transformer with double recurrent graph convolutional cooperative network for traffic flow prediction
Traffic flow prediction is an important part of ITS, accurate traffic flow prediction plays a crucial role in the development of ITS. It can not only...
-
Multi-scale attention graph convolutional recurrent network for traffic forecasting
In the backdrop of an ever-expanding urban transportation road network, the dramatic changes in traffic flow make traffic flow forecasting become a...
