1 Towards Efficient Ridesharing via Order-Vehicle Pre-Matching Using Attention Mechanism 2 LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data 3 Align Along Time and Space: A Graph Latent Diffusion Model for Traffic Dynamics Prediction...
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 ...
This paper proposes an attention mechanism based on the improved spatial-temporal convolutional neural network (AMSTCNN) for traffic police gesture recognition. This method focuses on the action part of traffic police and uses the correlation between spatial and temporal features to recognize traffic ...
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...
In this paper, we first investigate the phenomenon of the spatial-temporal initialization dilemma towards realistic visual tracking, which may adversely af
In this paper, we propose a facial expression recognition method based on spatial-temporal fusion with attention mechanism(STAFER) which is composed of the spatial feature extractor (SFE), the temporal feature extractor (TFE), and spatialtemporal fusion (STF). Firstly, a 10 layers neural network...
For adaptive aggregation, we propose a novel spatial-temporal attention mechanism. Extensive experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed method achieves superior results on these benchmarks. 展开 ...
1.模型结构 1.如何构建图,并基于self-attention学习node feature和edge feature 2.spatial mask & temporal mask 总结 贡献: 构建全连接图 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。
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 ...