本文总结了ICDM 2024有关时空数据(spatial-temporal data)的相关论文,如有疏漏,欢迎大家补充。 时空数据Topic:交通预测,事故预测,轨迹生成,POI查询,信控优化等内容。总计10篇,其中regular4篇,short5篇,Demo 1篇。 Regular 1 Towards Efficient Ridesharing via Order-Vehicle Pre-Matching Using Attention Mechanism 链接...
1 Prompt-Based Spatio-Temporal Graph Transfer Learning 2 Rethinking Attention Mechanism for Spatio-Temporal Modeling: A Decoupling Perspective in Traffic Flow Prediction 3 Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation 4 ByGCN: Spatial Temporal Byroad-Aware ...
In this paper, we propose a spatial-temporal fusion network for the group Re-ID. The network composes of the residual learning played between the CNN and the RNN in a unified network, and the attention mechanism which makes the system focus on the discriminative features. We also propose a...
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 ...
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism 因为太喜欢这篇文章了,所以再简单的写一遍。 本文用带有时空注意力机制的基于CNN的单目标跟踪器实现在线的多目标跟踪。为了online MOT,提出了一种基于CNN的框架。简单的把SOT应用至MOT会遇到计算效率和因为...
Spatial Multi-Omics Spatial-Temporal Transcriptomics Challenges and Prospects Data Standardization and Databases Artificial Intelligence (AI) in Data Interpretation Advantages and Disadvantages of Current Methods Graph Contrastive Learning and Multi-task Learning ...
The modeling of the self-attention mechanism focuses on the entire sequence and is therefore set as long-term features, while the modeling of the GRU focuses on temporal relationships and is therefore set as short-term features. Intuitively, the periodicity of long-term habits is not significant...
In this Review, we summarize the latest developments in the spatial and temporal characterization of solid catalysts using synchrotron radiation to uncover their structure and function. Attention is focused on applications using either X-rays or infrared light available from synchrotron radiation sources....
spatial-temporal position embedding node features 和edges通过spatial and temporal domain的self-attention mechanism 学习 使用spatial-temporal mask,降低了99%的复杂度。 0.MultiHeadedAttention recap 给定input featuref(t,i),分别经过3个fc,可以分别得到QKV. ...
Moreover, we incorporate the attention mechanism to enable cross-domain learning to capture both spatial-temporal relationships among the EEG electrodes and an adversarial mechanism to reduce the domain shift in EEG signals. To evaluate the performance of RODAN, we conduct subject-dependent, subject-...