Shi et al. [31] effectively performed spatiotemporal modeling of pedestrians by modeling the information of different pedestrians in the same scene as a spatial graph and movement of the same pedestrian across
Although accurate long-lead prediction of ENSO is essential, the intricate time-varying spatial correlations make it challenging. In this paper, we propose a novel deep learning architecture, called Adaptive Graph Spatial-Temporal Attention Network (AGSTAN), to model the extensive spatial–temporal ...
Firstly, an adaptive graph method is employed to capture the connectivity relationships beyond the human body skeleton. Furthermore, the Transformer framework is utilized to capture the dynamic temporal variations of the worker’s skeleton. To evaluate the model’s performance, six typical worker ...
However, a single attention mechanism cannot simultaneously capture the spatiotemporal attributes of traffic data, nor does it account for the influence of the urban traffic network’s spatial topology on the spatiotemporal correlations in traffic data. Graph convolutional neural network Traditional CNN ...
The reason is that we only take the overall information of the skeleton structure in the spatial dimension in our approach. In our future work, we will further investigate the feature extraction of each part of the skeleton, and consider how to re-establish a spatiotemporal graph model on ...
In vivo volumetric imaging of calcium and glutamate activity at synapses with high spatiotemporal resolution Adaptive optics (AO) corrects sample aberrations and allows high spatial resolution at depth in vivo. Here the authors report an AO method for Bessel focus; they apply AO Bessel focus scann...
To fill the gap, we innovatively propose a Dynamic Adaptive Graph Convolutional Transformer with a Broad Learning System (DAGCT-BLS), a GCN and Transformer-based model utilizing multivariate spatial dependence for multi-dimensional chaotic time series forecasting [paper]. The framwork of DAGCT-BLS ...
An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sensing of Environment, 2010, 114(11): 2610–2623. To Cite cuESTARFM in Publications Please cite the following reference: Gao, H., Zhu, X., Guan, Q., Yang, X., Yao, Y., Zeng, ...
The most physically consistent approach to model non-equilibrium flows relies on the direct numerical solution of the master equation5,6,9,10,11,12,13, whereby all the relevant spatial and temporal scales resulting from chemical and radiative processes are accounted for. Indeed, the availability of...
In light of the aforementioned problems, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is proposed and validated in this study; the model consists of a Non-Local Linear Regression (NL-LR) based weighted average module and a NL-LR based image Super-Resolution (SR) module to predi...