Inspired by the success of ViG, we propose a GNN-based multi-scale fusion network model (GCNCD) to extract graph-level features for remote sensing building change detection (CD). GCNCD builds bitemporal images into a graph structure. It then learns richer features by aggregating the features ...
Following the top-down paradigm, we propose GRMPose, a GCN-based real-time pose estimation framework for dairy goats . The framework adopts CSPNext as its backbone , which is highly efficient and potent for extracting features. In addition, a GCN-based coordination classification module (GCNCC)...
单任务GCN+GLU (Global)方法将GLU添加到单任务GCN (Global)方法中。使用该方法,RMSE和MAE均降低,分别为0.9842和0.6836,表明GLU单位可以提高预测性能。 多任务GCN + GLU (Global)方法考虑了出发和到达之间的相关性。该方法将出租车到达流预测作为相关任务,采用多任务学习策略,提高了出租车到达流预测的准确性。可以看...
dataset=Planetoid(root='G:/GCN_pytorch/Cora',name='Cora') print('classes num :',dataset.num_classes) print('edge feature num:',dataset.num_edge_features) print('edge nums:',dataset.data.edge_index.shape[1]/2) print('node feature nums:',dataset.num_node_features) print('node nums:'...
In this paper, we propose an active learning framework of graph convolutional network (GCN)-based zero-shot learning for image classification and design a new active learning algorithm called GAZL that can enable the zero-shot learning model to achieve a higher performance with a fixed amount of...
GCN for 3D Point Cloud Classification This repository is a toy PyTorch implementation of GCN-based 3D point cloud classification model. Requirements Python3==3.7 pytorch==1.4.0 tensorboardX==2.0 hdf5==1.10.4 Dataset To evaluate the model, ModelNet40 dataset in HDF5 format are required to be ...
To analyze these dynamic properties, this article proposes a dynamic network-based multitask rumor detection method called dynamic GCN-based multitask model with temporal event sharing for rumor detection (DGCN-TES). This method can effectively capture the dynamic patterns of relationships in ...
GCN for 3D Point Cloud Classification This repository is a toy PyTorch implementation of GCN-based 3D point cloud classification model. Requirements Python3==3.7 pytorch==1.4.0 tensorboardX==2.0 hdf5==1.10.4 Dataset To evaluate the model, ModelNet40 dataset in HDF5 format are required to be ...
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GraphSage是在论文Inductive Representation Learning on Large Graphs William中提出的一种归纳式的embedding表示训练方法。在上一篇所讲的GCN是transductive learning（直推式学习），即通过一个固定的图，直接训练每个节点的embedding。但是在很多图场景下，图节点是实时更新的，所以本文提出了inductive ...