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...
Spatial Graph Convolution Spatial Graph Convolutional Neural Network: Spatial Graph Convolution 加了一项归一化因子,主要的目的是不同子集对于结果的影响,最后结合之前的推导,得到公式5. Spatial Temporal Modeling Spatial Graph Convolutional Neural Network: Spatial Temporal Modeling 上一节讲的是空间卷积操作,这里重...
为了完成在 spatial temporal graph 上的卷积操作,我们也需要 the sampling function,and the weight function. 因为 temporal axis 的次序是显然的,我们直接将 label maplSTlST定义为: 3.4. Partition Strategies. 给定spatial temporal graph convolution 的高层定义,设计一种 partitioning strategy 来执行 the label ma...
[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 Synchronous Graph Convolutional Network STSGCN的核心思想是三点:1)在上一个时间步骤和下一个时间步骤将每个节点与自身连接起来,构建一个本地化的时空图。2)利用时空同步图卷积模块获取局域化的时空相关性。3)部署多个模块对时空网络序列的异构性进行建模。
Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn...
行人重识别阅读笔记之Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification,程序员大本营,技术文章内容聚合第一站。
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》,程序员大本营,技术文章内容聚合第一站。
It mainly consists of three parts: an ordinary differential equation-temporal convolutional network (ODE-TCN) module, a graph convolutional network-temporal convolutional network (GCN-TCN) module and an output module. The ODE-TCN module is composed of an integrator, a solver and a temporal ...
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classificat... 睡眠阶段分类对睡眠评估和疾病诊断至关重要。然而,如何有效地利用不同睡眠阶段的大脑空间特征和过渡信息仍然是一个挑战。特别是,由于对人类大脑的认识有限,为睡眠阶段的分类预先定义一个合适的大脑空间连接结构仍然是...