In order to get a better representation of the spatial–temporal skeletal features, this paper introduces a view transform graph attention recurrent networks (VT+GARN) method for view-invariant human action recognition. We design a view-invariant transform strategy based on the sequence to reduce ...
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...
Temporal Graph Attention(attn):一系列 L 层的图注意层通过聚合 L 跳时间邻域信息来计算 i 的嵌入。 在第l 层,输入为节点 i 的表示 h_{i}^{l-1}(t) 、当前时间 t 、带时间戳 t_{1},...,t_{N} 节点i 的邻域节点表示 {{ h_{1}^{l-1}(t-1)},...,h_{N}^{l-1}(t-1) }和特征...
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 ...
Temporal Graph Attention (attn):一系列L图注意层通过汇总来自其L跳时间邻域的信息来计算i的嵌入。第l层的输入是i表示 ,当前时间戳t,i邻域表示 以及时间戳 下对于在i的时间邻域中形成边的每个考虑的相互作用, 在此,φ(·)表示通用时间编码,k是级联运算符,zi(t)= emb(i,t)= h(L)i(t)。每层相当于执...
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 ...
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。
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》 预测交通流,该问题最大的挑战是交通流数据的高度非线性和复杂的关系模式。现存的预测方法缺乏对交通流动态时空关系的建模,于是本文提出一种带注意力机制的图卷积神经网络attentionbased...forcitywide crowd flows pre...
3 TEMPORAL GRAPH NETWORKS 根据(Representation learning for dynamic graphs: A survey)中的观点,动态图的神经模型可以被视为编码器-解码器对,其中编码器是一个函数,从动态图映射到节点嵌入,解码器将一个或多个节点嵌入作为输入,并进行特定于任务的预测,如节点分类或链接预测。本文的主要贡献是一种新颖的时间图网络...
CSGAT-Net models the physical environment and pedestrian behavior information in the scene as a semantic map, and it leverages graph attention networks to extract pedestrian interaction features. Finally, it predicts pedestrian future trajectories using a variational autoencoder. Comparative experiments ...