Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting.doi:10.1609/AAAI.V34I04.5758Weiqi ChenLing ChenYu XieWei CaoYusong GaoXiaojie FengAssociation for the Advancement of Artificial Intelligence (AAAI)National Conference on Artificial Intelligence...
As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven bench...
Sung F, Yang Y, Zhang L, Xiang T, Torr P, Hospedales T (2018) Learning to compare: relation network for few-shot learning. In:IEEE/CVF conference on computer vision and pattern recognition, CVPR, pp 1199–1208. Garcia V, Bruna J (2017) Few-shot learning with graph neural networks.h...
Pipeline of the semantic graph-based group activity recognition. From left to right: (a) object proposals are extracted from raw frames by a region proposal network [14]; (b) the semantic graph is constructed from text labels and visual data; (c) temporal factor is integrated into the graph...
An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relationEd-drissiya El-allaly aMourad Sarrouti cNoureddine En-Nahnahi aSaid Ouatik El Alaoui b
3.2 Graph Inference Inspired by [52], the graph inference is performed by using the mean field and computing the hidden states with Long Short-Term Memory (LSTM) net- work [22], which is an effective recurrent neural network. Let the semantic graph be G = (S, V, E), where S is ...
& Wang, S. SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding. Inf. Softw. Technol. 158, 107168 (2023). Article Google Scholar Li, L. et al. VulANalyzeR: Explainable binary vulnerability detection with multi-task ...
AttCoop-Q consists of the neighborhood graph convolutional network (NGCN) module and the attentive cooperation policy learning module. NGCN encodes the current situation, constructs a neighboring agent graph and uses the architecture of Neighborhood Graph Convolutional Network (GCN) to extracts ...
Convolutional neural networkAttention mechanismKnowledge graphs' incompleteness has motivated many researchers to propose methods to automatically infer missing facts in knowledge graphs. Knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the ...
More recently, motivated by the fact that the graph convolutional network (GCN) [37] can capture the relative influence and the potential spatial relationships in traffic scenarios, the graph attention network (GAT) [38] has been used in trajectory prediction [39,40,41], extracting the spatial...