其中,Gconv(⋅)是一个图卷积层。图卷积递归网络(Graph Convolutional Recurrent Network, GCRN)[71] 将LSTM网络与ChebNet [21] 结合在一起。扩散卷积递归神经网络(Diffusion Convolutional Recurrent Neural Network, DCRNN)[72] 将提出的扩散图卷积层(方程18)结合到GRU网络中。此外,DCRNN采用了编码器-解码器框架来...
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,轨迹相似度计算,物流配送以及时空图神经网络在金融,供应链,能源等领域应用的相关内容,如有疏漏,欢迎大家补充。 Full Research 1 Prompt-Based Spatio-Temporal Graph Transfer Learning 链接:arxiv.org/abs/...
11. Make Bricks with a Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity 作者:Binwu Wang, Pengkun Wang, Zhengyang Zhou, Zhe Zhao, Wei Xu, Yang Wang 机构:中国科学技术大学 关键词:交通预测,大规模时空图,子图 12. A Graph-based Representation Framework fo...
基于分解的多序列联合建模方法,利用矩阵分解的思路,该方法最早起源于Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction(NIPS 2016,TRMF)。整体思路如下图,将所有时间序列组成一个矩阵N*T,然后通过矩阵分解的方法,将原矩阵分解成两个子矩阵F(N*d)和T(d*T),其中d*T可以理解为d...
dynamic graph learningtraffic speed predictionspatial-temporal dataTraffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance...
3.4 Framework of Graph WaveNet 它由堆叠的时空层和输出层组成。时空层由图卷积层(GCN)和门控时间卷积层(gated temporal convolution layer)构成,门控时间卷积层由两个并行的时间卷积层(TCN- A和TCN-b)组成。通过叠加多个时空层,GraphWaveNet能够处理不同时间层次上的空间依赖性 ...
This is the implementation of Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation (ICML'23) in the following paper: Please also refer to this github linkhttps://github.com/HKUDS/GraphST Pytorch = 1.7.0 and Tensorflow = 1.15.3 (crime prediction task (ST-SHN)) ...
[TOC] Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification(CVPR2020) 行人重识别 行人重识别(Person Re-identification),简称为ReID,是利用计算机视觉技术判断图像或者视频序列中是否存在特定行人的技术。广泛被认为是一个图像检索的子问题。给定一个监控行人图像,检索跨设备下的该...
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation This is the implementation of Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation (ICML'23) in the following paper:RequirementsPytorch = 1.7.0 and Tensorflow = 1.15.3 (crime prediction task (ST-SHN))...
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition回到顶部 摘要动态人体骨架模型带有进行动作识别的重要信息,传统的方法通常使用手工特征或者遍历规则对骨架进行建模,从而限制了表达能力并且很难去泛化。作者提出了一个新颖的动态骨架模型ST-GCN,它可以从数据中自动地学习空间和时间的pattern...