Graph convolutional networkEncoder-decoder temporal convolutional networkUrban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. In this paper, we propose a Deep Temporal Multi-Graph Convolutional Network (DT-MGCN) model which ...
这样,每一个时刻都可以建图,所有时刻的图为G={G1,G2,…,Gm},每个时刻包括 spatial affinity graph 和 feature affinity graphGj={Gsj,Gfj}(j=1,2,…,m) Multi-view Graph Convolution Network The overview of the proposed CTVI 对于spatial affinity graph,使用独立的 spatial convolution network \mathbf{...
Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification论文笔记(时空图卷积) 本篇论文发表在CVPR 2020,作者将图卷积这种方法用在了行人重识别领域(基于视频的ReID),作者单位分别为:中山大学、鹏程实验室、香港中文大学、华为诺亚方舟实验室等单位!我找了很多图卷积相关的论文,发现GCN应...
[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...
To overcome these limitations, we propose a multi-scale spatiotemporal graph convolution network (MST-GCN), which consists of a multi-scale block, several spatial-temporal blocks and a fusion block. We first divide the extracted features into several groups based on their domain categories, and ...
现存交通预测方法缺陷:一些交通预测方法(ARIMA、Kalman filtering model,etc)只关注了交通状况的动态变化而忽视了空间关系,导致交通状态的变化不被道路网约束,同时一些模型尝试使用卷积神经网络进行空间性建模,但这些模型一般只使用于欧几里得类型的数据(规则矩阵、图像等),无法在拓扑结构的城市交通网络中运作。
In this paper, we propose a Multi-Scale Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for facial expression spotting. To extract more robust motion features, we track both short- and long-term motion of facial muscles in compact sliding windows whose window length adapts to the temporal...
HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition (MM ’21) 基于生理信号的多媒体刺激下人的情绪研究是一个新兴领域,基于多模态信号的情绪识别已取得重要进展。然而,如何充分利用时空特征之间的互补性进行情感识别,以及如何对多模态信号之间的异质性和相关性...
To address the challenge of spatial-temporal correlation in traffic flow forecasting, we propose a novel deep learning model, the multi-head self-attention spatiotemporal graph convolutional network (MSASGCN). It can learn the temporal and spatial dependencies of dynamic traffic data and effectively ...
GCN用于学习复杂的拓扑结构以捕获空间依赖性,而GRU用于学习交通数据的动态变化以捕获时间依赖性。T-GCN的代码:GitHub - lehaifeng/T-GCN: Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method 现有的流量预测方法:自回归综合移动平均(ARIMA)模型,SVM 和部分神经网络,考虑了交通的动态变化而...