Graph attention networkRamp predictionWind power ramp eventsWind propagation? 2024 Elsevier LtdThe increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of win
Graph Neural Network for Traffic Forecasting: A Survey —— temporal GNN 摘要交通预测对于智能交通系统的成功与否至关重要。深度学习模型,包括卷积神经网络和递归神经网络,已广泛应用于交通预测问题,以模拟空间和时间依赖性。近年来,为了对交通系统中的图结构… 马东什么 2019CVPR_Trilinear Attention Sampling Network...
Initially, the model adaptively adjusts spatiotemporal weight distribution using a meticulously designed spatiotemporal attention mechanism, effectively capturing dynamic spatiotemporal correlations in traffic data. Subsequently, it integrates graph convolutional neural networks (GCNs) with long short-term ...
Each space–time block is composed of two graph attention networks and a gated recurrent unit, which are used to extract the spatial and temporal characteristics of road traffic flow respectively, while adding residual connections to prevent the gradient from disappearing. Then, with the traffic ...
针对Temporal Transformer,就正常用Transformer即可。 【KEY】针对Spatial Transformer 首先有一个观察:self-attention可以看作是在无向全连图上传递信息。直观理解就是计算QK^T时即为计算两个节点之间的相似度/信息传递量。下面通过公式说明: 假设query vector为 qi,key vector为 ki,value vector为 vi 。那么,不妨设...
Graph Attention Block左侧为local graph attention layer,右侧为global graph attention layer。 A. Temporal Convolutional Network 这部分和videopose3D有点像,卷积同样采用空洞卷积以扩大感受野,看了下参考文献,确实是在其基础上改进的,主要区别是作者将输入的2D pose sequence表示为三维向量(T, N, C),T是接受域...
in this work, we propose a novel GNN-based model, Attention-adjusted Graph Spatio-Temporal Network(AGSTN). In AGSTN, multi-graph convolution with sequentiallearning is developed to learn time-evolving STC. Fluctuationmodulation is realized by a proposed attention adjustmentmechanism. Experiments on ...
In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the
3.5 Spatio-Temporal Graph Transformer 时间transformer可以单独模拟每个行人的运动动力学,但不能考虑空间交互作用;spatial Transformer利用TGConv处理人群交互,但很难推广到时间序列。行人预测的一个主要挑战是建模耦合时空交互作用。行人的空间和时间动态密切相关。例如,当一个人决定她的下一个动作时,她首先会预测她的...
et al. CSGAT-Net: a conditional pedestrian trajectory prediction network based on scene semantic maps and spatiotemporal graph attention. Neural Comput & Applic 36, 11409–11423 (2024). https://doi.org/10.1007/s00521-024-09784-x Download citation Received24 May 2023 Accepted25 March 2024 ...