Graph data is an irregular structural data type that is broadly used in various realistic scenarios to represent the complex interrelationships or topological structures inside the data. As the topological structures of the graphs are usually different from each other, it is difficult to handle the ...
针对人体执行动作时可能存在的小幅度姿态调整和局部运动差异,使用EdgeConv模块能够捕捉到节点之间的关系,有助于更好地理解图数据的拓扑结构。EdgeConv模块包括以下主要步骤:输入特征X经过时间池化后,输入到Get graph_feature模块获得每个节点的局部图特征。使用KNN算法计算每个节点与其K个最近邻节点之间的关系,构建包含这种关...
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN...
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...
There exist a few deep learning models that are very successful in predicting flow fields of complex physical models, yet most of these still exhibit large errors compared to simulation. Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure ...
This repository is the offical tensorflow implementation ofMGTnet:Image-Guided Human Reconstruction via Multi-Scale Graph Transformation Networks(TIP 2020). Introduction 3D human reconstruction from a single image is a challenging problem. Existing methods have difficulties to infer 3D clothed human models...
Code for paper "GraphFit: Learning Multi-scale Graph-convolutional Representation for Point Cloud Normal Estimation" - UestcJay/GraphFit
In this paper, we establish a new framework that generalizes distance correlation — a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments — to the Multiscale Graph Correlation (MGC). By ...
论文标题:Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Network 地址:arxiv.org/abs/1906.0217 前言:作者的主要贡献就是设计了在块kyrlov子空间形式中推广谱图卷积和深度GCN,缓解了过平滑问题,并提升了GNN的表现。这次笔记我写的很细致,公式比较多,观看费劲原谅敬请。 图上滤波简述: 简单来说...
Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this article, we present an end-to-end, tile-based visual analytic approach called graph mapping that utilizes cluster computing to turn ...