Interpretable Spatial|Temporal Graph Convolutional Network for ...

Interpretable Spatial–Temporal Graph Convolutional Network for ...

This paper develops a semi-supervised graph neural network model termed the Interpretable Spatial–Temporal Graph Convolutional Network (IST-GCN).

[2402.19237] Context-based Interpretable Spatio-Temporal Graph ...

We present a Context-based Interpretable Spatio-Temporal Graph Convolutional Network (CIST-GCN), as an efficient 3D human pose forecasting model based on GCNs.

Context-Based Interpretable Spatio-Temporal Graph Convolutional ...

Context-based Interpretable Spatio-Temporal Graph Convolutional Network for. Human Motion Forecasting. Edgar Medina, Leyong Loh, Namrata Gurung, Kyung Hun Oh ...

Interpretable temporal graph neural network for prognostic ...

To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while ...

Interpretable Spatial-Temporal Attention Graph Convolution Network ...

Instead of using CNNs, this model uses interpretable hierarchical graph convolution networks (GCN). It is capable to handle non-Euclidean hierarchical data ...

Spatial Temporal Graph Convolutional Networks (ST-GCN)

Explaination for the paper “Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition” [1] (aka. ST-GCN) as well ...

Interpretable Spatial–Temporal Graph Convolutional Network for ...

Download Citation | On Oct 1, 2024, Rucong Xu and others published Interpretable Spatial–Temporal Graph Convolutional Network for System Log ...

Context-based Interpretable Spatio-Temporal Graph Convolutional ...

Human motion prediction plays a critical role in autonomous driving, robotics, and safety applications. In the recent past, several methods for human motion ...

GitHub - QualityMinds/cistgcn: Official implementation of the paper

Official implementation of the paper: CISTGCN - Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting ...

Context-based Interpretable Spatio-Temporal Graph Convolutional ...

Human motion prediction is still an open problem extremely important for autonomous driving and safety applications. Due to the complex spatiotemporal ...

Grace: Interpretable Root Cause Analysis by Graph Convolutional ...

Our work has three aims. First, to more accurately localize root causes using a Spatial-Temporal Graph Convolutional Network (STGCN). To the best of our ...

What is Spatial-Temporal Graph Convolutional Networks (ST-GCN)

Spatial-Temporal Graph Convolutional Networks (ST-GCN) enable deep learning on graph-structured data, capturing complex relationships and patterns in ...

Context-based Interpretable Spatio-Temporal Graph Convolutional ...

Context-based Interpretable Spatio-Temporal Graph Convolutional Network for. Human Motion Forecasting. Supplementary Material. 1. Additional Experiments. 1.1 ...

wanjinchang/st-gcn: Spatial Temporal Graph Convolutional ... - GitHub

Interpretable 3d human action analysis with temporal convolutional networks. In BNMW CVPRW. Training. To train a new ST-GCN model, run. python main.py ...

Interpretable Spatial-Temporal Attention Graph Convolution Network ...

In this paper, we present an interpretable general framework STAH (Spatial-Temporal Attention Graph Convolution network for Hierarchical demand forecast).

Multi-Scale Spatial Temporal Graph Convolutional Network ... - AAAI

Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent mod- eling ability of non-Euclidean data. As the ...

Spatial temporal graph convolutional networks for skeleton-based ...

We propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous ...

Multi-View Spatial-Temporal Graph Convolutional Networks With ...

3) Most deep learning methods, especially related graph neural network models, ignore the importance of model interpretability to the brain. There have been ...

[PDF] Spatial Temporal Graph Convolutional Networks for Skeleton ...

A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by ...

Physics-Informed Spatio-Temporal Graph Convolutional Network for ...

prediction accuracy, stability, and interpretability. Index Terms—pedestrian movements, trajectory prediction,. physics-informed ...