此外,我们最终的时空 Transformer 网络 (ST-TR) 在所有数据集 上实现了最先进的性能,使用相同的输入联合信息和流设置的方法,并在添加骨骼信息时与最先进的方法相当。由于仅涉及自注意力模块的配置被证明是次优的,因此未来可能的工作是搜索一个统一的 Transformer 架构,能够在各种任务中替换图卷积。
CIKM 2024于2024年10月21号-10月25号在美国爱达荷州博伊西举行(Boise, Idaho, USA) 本文总结了CIKM 2024有关时空数据(spatial-temporal data)的相关论文,主要包含交通预测,插补,事故预测,气象预测,轨迹相…
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 作者:Li Lin,Kaiwen Xia,Anqi Zhen...
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). ...
However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that ...
WSDM 2024 2023 | 时空数据(Spatial-Temporal)和时间序列(Time Series)论文总结 论文数据seriesspatialtime WSDM 2024于2024年3月4日-3月8日在墨西哥梅里达(Mérida, México)正在举行。目前官网已经放出了所有被录用论文的表单(链接在相关链接给出)。本次会议共收录112篇论文。 时空探索之旅 2024/11/19 1390 WWW...
ASTNAT: an attention-based spatial–temporal non-autoregressive transformer network for vehicle trajectory prediction. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10548-w Download citation Received04 January 2024 Accepted03 October 2024 Published26 November 2024 DOIhttps://doi...
Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp 3634–3640 Guo S, Lin Y, Feng N, et al (2019) Attention based ...
【论文阅读】STAR: Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction Weibo Mao 上海交通大学 信息与通信工程硕士 来源:ECCV 2020 1、背景 行人轨迹预测问题,需要考虑单个行人的时间信息以及时间点上多个行人之间行为交互关系。STAR要点:在时间维度上,对每个行人单独考虑,应用tempora...
Scalable Trajectory-User Linking with Dual-Stream Representation Networks Efficient Traffic Prediction through Spatio-Temporal Distillation Correlation Attention Masked Temporal Transformer for User Identity Linkage using Heterogeneous Mobility Data POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI ...