Subsequently, the spatial learning layer utilizes graph attention networks to describe the correlation among nodes. Additionally, an adaptive fusion module is implemented to equalize the impact of diverse temporal patterns. Finally, comprehensive experiments are carried out on two real-world datasets. The results affirm the efficacy ...
Continuous sign language recognition (CSLR) is challenging due to the complexity of video background, hand gesture variability, and temporal modeling difficulties. This work proposes a CSLR method based on a spatial-temporal graph attention network to focus on essential features of video series. The...
A spatial-temporal graph attention network for protein–ligand binding affinity prediction based on molecular geometry. Multimedia Systems 31, 94 (2025). https://doi.org/10.1007/s00530-024-01650-z Download citation Received09 August 2024 Accepted31 December 2024 Published04 February 2025 DOIhttps:/...
原文:(PDF) Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 现有的交通流预测方法大多缺乏对交通数据的动态时空相关性进行建模的能力,因此无法得到令人满意的预测结果。因此这篇文章提出了一种新的基于注意力的时空图卷积网络(Attention Based Spatial-Temporal Graph Convolutiona...
Modeling the Temporal Dependency: 作者首先使用GRU捕捉数据的局部时间依赖,并且对 t 时刻GRU的输入 X_t 和隐藏变量 H_{t-1} 先使用上文的GCN提取空间依赖: \begin{align}&\tilde{X}_t=f_a(A,X_t),\\&\tilde{H}_{t-1}=f_a(A,H_{t-1})\end{align}\tag{5} 然后对于每个节点的时间序列,单...
Location-based Graph Neural Network 更新边的特征 更新点的特征 更新全局图的特征 Spatial-temporal Attention Net 对于时间切片 对于空间切片 使用注意力机制,X是3维特征矩阵,对于每一个交易记录 使用时间注意力机制,更新时间切片,在此基础上在使用空间注意力机制 ...
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
To this end, the spatial-temporal graph neural ODE network (STG-NODE) proposed herein integrates well-designed components to overcome these challenges. As shown in Figure 1, compared with the traditional methods, STG-NODE has excellent advantages in terms of accurately identifying key actions. First...
Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3634–3640 Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spati...
The graph structures consider incidence dynamic relationships of both inflows and outflows. Then we design a novel dynamic graph recurrent convolutional neural network model, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures ...