To address the above problems, we propose a novel Adaptive Spatial–Temporal Transformer Network (ASTTN) designed to effectively capture dynamic spatial–temporal features and improve the model's predictive adaptability under different systems. Considering the comprehensive temporal features, we design a ...
2.Spatial-Temporal Transformer Network 这是STTN的核心部分,通过一个多头 patch-based attention模块沿着空间和时间维度进行搜索。transformer的不同头部计算不同尺度上对空间patch的注意力。这样的设计允许我们处理由复杂的运动引起的外观变化。例如,对大尺寸的patch(例如,帧大小H×W)旨在修复固定的背景;对小尺寸的patch...
在这项工作中,我们提出了一种新颖的Spatial Temporal Transformer Network(ST-TR),它使用变换器自注意算子对关节之间的依赖关系进行建模。 在我们的 ST-TR 模型中,空间自注意力模块 (SSA) 用于理解不同身体部位之间的帧内交互,以及时间自注意力模块 (TSA) 来模拟帧间相关性。 两者结合在一个双流网络中,该网络...
12 Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting 13 Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting 14 Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity 15...
Temporal Transformer implementation corresponds toST-TR/code/st_gcn/net/temporal_transformer.py. Set in/config/st_gcn/nturgbd/train.yaml: attention: False tcn_attention: True only_attention: True all_layers: False to run the temporal transformer stream (T-TR-stream). ...
12 Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting 作者:Jing He,Junzhong Ji,Minglong Lei 关键词:气象预测,知识蒸馏 13 Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting ...
3. MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization 作者:Dongcheng Zou (Beihang University)*; Senzhang Wang (Central South University); li xuefeng (Beihang university); Hao Peng (Beihang University); Yuandong Wang (Tsinghua Universi...
The proposed spatial-temporal graph Transformer module fuses the temporal and spatial features of the object to enhance the contextual features and correlation between temporal and spatial feature variations, recognizing the noise spoofing interference to enhance the tracking performance. Meanwhile, our ...
Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling ...
18 SSL-STMFormer Self-Supervised Learning SpatioTemporal Entanglement Transformer for Traffic Flow Prediction 19 RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputation 20 Integrating Personalized Spatio-Temporal Clustering for Next POI Recommendation 21 Traffic Scenario Logic:...