While node attributes, which record valuable information of traffic conditions, have not been fully exploited to guide the learning of better graph structure. In this paper, we propose an Adaptive Spatio-Temporal graph neural Network, namely Ada-STNet, to first derive optimal graph structure with ...
While node attributes, which record valuable information of traffic conditions, have not been fully exploited to guide the learning of better graph structure. In this paper, we propose an Adaptive Spatio-Temporal graph neural Network, namely Ada-STNet, to first derive optimal graph structure with ...
This research proposes an optimizing wind power prediction model through attention mechanism and spatiotemporal graph neural networks. Initially, the spectral clustering and a self-adjacency matrix to construct the graph nodes and edges. Subsequently, the proposed ASTGNN combined from graph convolutional ...
Two-Stream Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition 回到顶部 摘要 基于骨架的动作识别因为其以时空结合图(spatiotemporal graph)的形式模拟了人体骨骼而取得了显著的效果。 在现有的基于图的方法中,图的拓扑结构是手动设置的,而且在所有层以及输入样本中是固定不变的。这样的方法在用...
Two-Stream Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition 回到顶部 摘要 基于骨架的动作识别因为其以时空结合图(spatiotemporal graph)的形式模拟了人体骨骼而取得了显著的效果。 在现有的基于图的方法中,图的拓扑结构是手动设置的,而且在所有层以及输入样本中是固定不变的。这样的方法在用...
The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, ...
In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. However, in existing GCN-based methods, the topology of the graph is set manually, and it is fixed over all layers and in...
et al. PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 1–9 (2019). Article Google Scholar Sunkin, S. M. et al. Allen Brain Atlas: An integrated spatio-temporal portal for exploring the central ...
, explained in Section “Neural operators”. On the one hand, this approach allows for partially preserving the learned physics. On the other hand, it enables surrogate adaptation and knowledge transfer from one temporal scale to another, speeding up the training process of the entire network....
The CNN-BiLSTM model was constructed by fusing spatiotemporal techniques [35]. The data was resampled using the NCR-SMOTE method, and feature selection was achieved through a recursive feature elimination method based on extreme random numbers. The results indicated that while the model’s temporal...