To make it more distinguishable, we introduce a multi-scale architecture in the graph block to learn richer geometric features. Our method outperforms competitors with the state-of-the-art accuracy on various benchmark datasets, and is quite robust against noise, outliers, as well as the ...
这篇文章提出了一种基于多尺度图卷积网络的3D人体运动预测方法——dynamic multiscale graph neural network (DMGNN)。该网络结构分为编码器和解码器,其中编码器由一系列多尺度图计算单元(multiscale graph computational unit,MGCU)组成,解码器由图时序门单元(graph-based GRU)组成。该方法通过MGCU提取不同尺度的人...
论文标题:Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning论文作者:Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan论文来源:2021, IJCAI论文地址:download 论文代码:download 1 Introduction...
提出一种新颖的两流时空图卷积网络(2S-ST-AGCN) 2S-ST-AGCN的作用是从视频中提取身体特征以构建关节和骨骼的模型 在广泛的临床实验中,此方法远远优于现有的帕金森评估 引言: 现有的帕金森临床实践面临两个问题: 由训练有素的专家对运动进行全面评估至少需要半小时,所获得的结果在主观评分者之间也有很大区别 出行限...
PyTorch implementation of paper "GraphFit: Learning Multi-scale Graph-convolutional Representation for Point Cloud Normal Estimation", ECCV 2022. Installation Clone this repo: The code is tested with Ubuntu16.04, Python3.7, PyTorch == 1.6.0 and CUDA == 10.2. We recommend you to useanacondato ma...
题目:Multi-Scale Sparse Conv Learning for Point Cloud Compression and Super-Resolving 报告人:李竹,密苏里大学堪萨斯分校教授 时间:2023年12月20日(星期三)10:30-11:30 地点:上海交通大学闵行校区软件大楼5楼人工智能研究院500会议室 主持人:晏轶超,...
machine-learningdeep-learningtensorflowpytorchdeepwalkconvolutional-layersconvolutional-neural-networksngcnconvolutionalgcnnode2vecpytorch-cnngraph-attention-networksmulti-scalegraph-representation-learningwalkletgraph-convolutiongraph-attentionwalkletsmixhop UpdatedNov 6, 2022 ...
global-global:对比不同视角的图编码; multi-scale:对比来自一个视图的图编码与来自另一个视图的中间编码;使用 DiffPool 层计算中间编码; hybrid:使用 local-global 和 global-global 模式; ensemble modes:对所有视图,从相同视图对比节点和图编码。修改历史2022-03-27 创建文章2022-06-10 精读论文...
Multi-scale Spatial and Temporal Feature Aggregation Graph Convolutional Network for Skeleton-Based Action Recognition In the field of deep learning, skeleton data is widely used for action recognition. Currently, the recognition of human skeleton action based on Graph Conv... Y Du,M Zhang,B Li ...
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning 爱吃鱼的猫 4 人赞同了该文章 近年来基于对比学习的图神经网络技(GCL)术取得了巨大成功,其可以有效减少图神经网络对有标签数据的依赖,但是目前的研究中仍存在一下限制:1)现有的基于MI的对比方法需要计算正负样本对得分,导致...