GAT和GCN是最常用的模型 二、Spatial-based convolution 回顾一下卷积神经网络的卷积方式: 卷积核与第i层的特征矩阵对应元素相乘相加 得到i+1层的特征矩阵 对应到图上,就是运用节点周围的节点来更新下一层的feature map Aggregate: 用邻居特征更新下一層的hidden state Readout: 把所有nodes的feature集合起來代表整個...
关系。通常有两种做法: 但是常用的算法是GAT和GCN。Spatial-basedconvolution 用neighbor feature更新下一代... Networks)GraphAttention NetworksGIN(GraphIsomorphismNetwork) 这篇文章主要是在理论上分析了什么样的做法会work,在 Paper Notes: A Comprehensive Survey on Graph Neural Networks ...
对于GNN来说,其中一个重点是graph filtering layer里面的Spatial-based Graph Filters,这部分与书后面的时空GNN等方向也有很大关联,所以这里将其单列出来用一篇笔记细讲。 我们先回忆GNN历史上的第一种filter,这种filter是“spatially localized(即对于每一个点,设计出的函数在空间上基本只和与该点相距很近的点有关)...
(1)利用GCN来模拟人体不同部位在一帧内和帧间的潜在关系,为人们提供更具鉴别力和鲁棒性的信息(2)提出了时空GCN框架来联合建模视频层的整体斑块关系和帧级的单个帧的结构信息,该框架可以学习斑块之间的区分和鲁棒的时空关系,从而促进基于视频的Re-ID。 模型设计 设计了3个分支 - 上部分支是用于从相邻帧上的斑块...
Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in modelling spatial dependencies. In most existing approaches, GCN models spatial dependencies in the traffic network with a fixed adjacency matrix. However, the spatial ...
Then, Splane constructs a GCN model from the adjacency matrix and the cell type composition of cells/spots, and employs an adversarial learning algorithm46 to learn the latent features shared across all of the analyzed ST slices; we term this a joint analysis scheme. Next, Splane applies a K...
【论文学习】ST-GCN:Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,程序员大本营,技术文章内容聚合第一站。
输入的数据首先进行batch normalization,然后在经过9个ST-GCN单元,接着是一个global pooling得到每个序列的256维特征向量,最后用SoftMax函数进行分类,得到最后的标签。每一个ST-GCN采用Resnet的结构,前三层的输出有64个通道,中间三层有128个通道,最后三层有256个通道,在每次经过ST-CGN结构后,以0.5的概率随机将特征...
However, most of GCN-based methods are inflexible to encode semantic relations because of additional annotations (i.e., handcrafted relation classes) involved when classifying the relationship between objects. As shown in Fig. 3a, all relationships appearing in the image will be classified into one...
实验结果表明,DS-GCN在多个基准测试中取得了显著的性能提升。通过引入节点和边类型感知的自适应图模块,DS-GCN在不同骨干网络中均提升了动作识别准确率,尤其是在区分相似动作上表现更佳。与其他ST-GCN变体相比,DS-GCN在NTU和Kinetics数据集上以更少的参数实现了更高的准确率。此外,分支特定的权重配置和全层语义编码...