This is the first time that graph convolutional networks are introduced into the AE source localization task. Data generated by AE sensor networks is represented by a graph structure, in which the temporal feat
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,程序员大本营,技术文章内容聚合第一站。
论文翻译:Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,程序员大本营,技术文章内容聚合第一站。
The pioneering work27 concerning spatial-temporal graph convolutional networks (ST-GCNs), which encapsulate human skeleton data within graph frameworks, is particularly important. In this approach, a GCN is used for skeleton-based action recognition. This impetus has pushed GCN-based methods to the ...
Spatial Graph Convolutional Neural Network: Spatial Temporal Modeling 上一节讲的是空间卷积操作,这里重新回到了时间层面,对于t时刻的结点 v_{ti}的邻居结点需要加上在时间点q上的 v_{qj} 满足的条件为 d(v_{ti}, v_{tj}) \leq K ,且满足时间关系 |q-t| \leq [\Gamma/2] (取整)。 \Gamma 是...
时空图建模 Spatial-Temporal Synchronous Graph Convolutional Networks (AAAI 20) Canvas 时空数据挖掘中的自适应图学习 想介绍此类相关的方法已有一段时间,最近整理论文工作,借此机会,分享一下。 引言目前图神经网络在时空数据挖掘领域取得了突出表现,主要得力于捕获空间依赖能力的增强。一句话可以总结为… 西南交一枝....
This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the...
为了完成在 spatial temporal graph 上的卷积操作,我们也需要 the sampling function,and the weight function. 因为 temporal axis 的次序是显然的,我们直接将 label maplSTlST定义为: 3.4. Partition Strategies. 给定spatial temporal graph convolution 的高层定义,设计一种 partitioning strategy 来执行 the label ma...
Then, the graph convolu-tional networks (GCN) are constructed for spatial structure feature reasoning in a single frame, which is consecutively followed by long short-term memory (LSTM) networks for temporal motion feature learning within the sequence. Moreover, the attention mechanism is further ...
To address these limitations, a knowledge-embedded spatial–temporal graph convolutional networks (KEST-GCN) method is proposed. In KEST-GCN, the relationship triplets are established based on the system structure knowledge and sensor position information. Then, these triplets are transformed into low-...