近年来,transformer网络在NLP领域占据主导地位[43,10,26,52,50]。Transformer模型完全抛弃了递归性,而将注意力集中在跨时间step的关注上。该架构允许长期依赖建模和大规模并行训练。transformer结构也已成功应用于其他领域,如股票预测[30]、机器人决策[12]等。STAR将Transformer的思想应用于图序列。我们在一个具有挑战性...
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction 代码梳理 import copy #导入拷贝库 import numpy as np #导入数值计算库,以后调用采用np缩写 import torch #导入torch库包含一些常用的数据类型 import torch.nn as nn #导入torch中的网络层以后调用采用nn缩写,例如nn.liner() import ...
图一、STAR中的temporal transformer以及spatial transformer。第一眼看上去右边这个和GAT很像。 2、模型设计 构建图:空间图中的边表示两个行人之间的距离小于一定阈值。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...
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory PredictionUnderstanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. This is challenging because it requires effectively modeling the......
Moreover, a Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and other dynamic adjacency relations between intersections. The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST ...
Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow ForecastingGRAPH neural networksTRAFFIC estimationTRAFFIC flow... C Wang - 《Electronics》 被引量: 0发表: 2024年 Traffic Flow Forecasting Based on Transformer with Diffusion Graph Attention Network Traffic Flow Forecasting...
STCGCN: a spatio-temporal complete graph convolutional network for remaining useful life prediction of power transformer Mengda Xing, Weilong Ding, Tianpu Zhang, Han Li International Journal of Web Information Systems ISSN: 1744-0084 Article publication date: 6 July 2023 Permissions Issue publica...
Graph-aware transformer for skeleton-based action recognition[J]. The Visual Computer, To be published.. Google Scholar [9] YAN Sijie, XIONG Yuanjun, and LIN Dahua. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]. The 32nd AAAI Conference on Artificial ...
It presents a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture real-time traffic conditions as well as the directionality of traffic flows. Furthermore, different spatial dependency patterns can ...
11. Next POI Recommendation with Dynamic Graph and Explicit Dependency 12. Scalable Spatiotemporal Graph Neural Networks 13. Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling 14. c-NTPP: Learning Cluster-Aware Neural Temporal Point Process 15. Trafformer: Unify Time and Spa...