Search
Search Results
-
Temporal-visual proposal graph network for temporal action detection
Temporal action detection is usually divided into two stages: temporal action proposal generation and proposal classification. Most methods consider...
-
Spatial-temporal graph transformer network for skeleton-based temporal action segmentation
Temporal action segmentation (TAS) of minute-long untrimmed videos involves locating and classifying human action segments using multiple action...
-
Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
-
Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network
Spatio-temporal graph data can be analyzed by effectively mining for realizing spatio-temporal graph data prediction. It is of great significance to...
-
Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
-
Towards efficient simulation-based constrained temporal graph pattern matching
In the context of searching a single data graph G , graph pattern matching is to find all the occurrences of a pattern graph Q in G , specified by a...
-
Temporal graph patterns by timed automata
Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph...
-
Temporal-order association-based dynamic graph evolution for recommendation
Modeling the interactions between users and items to accurately predict a user preference on items is very crucial for improving the performance of...
-
Multi-attention gated temporal graph convolution neural Network for traffic flow forecasting
Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic...
-
Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection
Analyzing multi-channel data related to the industrial chain through graph representation learning is of significant value for industrial chain risk...
-
Structure-adaptive graph neural network with temporal representation and residual connections
Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions...
-
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...
-
Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution
An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load...
-
A relation enhanced model for temporal knowledge graph alignment
Entity alignment (EA) aims to find entities that point to the same object in multiple knowledge graphs (KGs), i.e., finding equivalent entity pairs....
-
Attention-based spatial-temporal graph transformer for traffic flow forecasting
Traffic forecasting is significant for establishing intelligent traffic systems. However, the complex spatial-temporal relationships of traffic flow...
-
Dynamic temporal position observant graph neural network for traffic forecasting
Spatio-temporal forecasting has several applications in neurology, climate, and transportation. One classic example of such a learning assignment is...
-
Enhancing academic performance prediction with temporal graph networks for massive open online courses
Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more...
-
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting
Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models...
-
ISTGCN: Integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network
To effectively estimate traffic patterns, spatial-temporal information must consider the complex spatial connections on road networks and...
-
Spatial-temporal graph-guided global attention network for video-based person re-identification
Global attention learning has been extensively applied in video-based person re-identification due to its superiority in capturing contextual...
