中文版 | English

在结果中检索

CSpace

浏览/检索结果: 共14条，第1-10条 帮助

已选(0)清除 条数/页：   排序方式：
CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks 期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 卷号: 226, 页码: 11
作者:  Zhu, Xiaofei;  Li, Chenghong;  Guo, Jiafeng;  Dietze, Stefan
收藏  |  浏览/下载：13/0  |  提交时间：2023/12/04
Network representation learning  Deep learning  Graph convolutional networks  Node classification  
DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 11
作者:  Zhu, Yida;  Luo, Haiyong;  Chen, Runze;  Zhao, Fang
收藏  |  浏览/下载：11/0  |  提交时间：2023/12/04
Convolutional denoising autoencoders (CDAEs)  global-attention mechanism  graph convolutional networks  human activity recognition (HAR)  multisensor modality  self-attention mechanism  
Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 2, 页码: 2009-2023
作者:  Wen, Yu-Hui;  Gao, Lin;  Fu, Hongbo;  Zhang, Fang-Lue;  Xia, Shihong;  Liu, Yong-Jin
收藏  |  浏览/下载：17/0  |  提交时间：2023/07/12
Skeleton  Feature extraction  Joints  Convolutional codes  Topology  Training  Sparse matrices  Action recognition  graph convolutional neural networks  spatio-temporal attention  non-local block  skeleton sequence  
附加特征图增强的图卷积神经网络 期刊论文
计算机学报, 2023, 卷号: 46, 期号: 9, 页码: 1900
作者:  孙隽姝;  王树徽;  杨晨雪;  黄庆明;  郑振刚
收藏  |  浏览/下载：7/0  |  提交时间：2024/05/20
graph representation learning  graph neural networks  message passing  graph convolutional networks  node classification  图表示学习  图神经网络  信息传播  图卷积网络  节点分类  
Location-aware convolutional neural networks for graph classification 期刊论文
NEURAL NETWORKS, 2022, 卷号: 155, 页码: 74-83
作者:  Wang, Zhaohui;  Cao, Qi;  Shen, Huawei;  Xu, Bingbing;  Cen, Keting;  Cheng, Xueqi
收藏  |  浏览/下载：17/0  |  提交时间：2023/07/12
Graph classification  Convolutional neural networks  Location-aware  
Exploring rich structure information for aspect-based sentiment classification 期刊论文
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2022, 页码: 21
作者:  Zhu, Ling;  Zhu, Xiaofei;  Guo, Jiafeng;  Dietze, Stefan
收藏  |  浏览/下载：31/0  |  提交时间：2022/12/07
Aspect-based sentiment classification  Graph convolutional networks  Attention mechanism  Sentiment analysis  
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey 期刊论文
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 卷号: 9, 期号: 2, 页码: 205-234
作者:  Liu, Xin;  Yan, Mingyu;  Deng, Lei;  Li, Guoqi;  Ye, Xiaochun;  Fan, Dongrui
收藏  |  浏览/下载：34/0  |  提交时间：2022/06/21
Efficient training  graph convolutional networks (GCNs)  graph neural networks (GNNs)  sampling method  
Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting 期刊论文
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 卷号: 11, 期号: 2, 页码: 17
作者:  Zhang, Chi;  Zhou, Hong-Yu;  Qiu, Qiang;  Jian, Zhichun;  Zhu, Daoye;  Cheng, Chengqi;  He, Liesong;  Liu, Guoping;  Wen, Xiang;  Hu, Runbo
收藏  |  浏览/下载：25/0  |  提交时间：2022/12/07
traffic flow forecasting  spatial-temporal prediction  graph convolutional networks  augmented multi-component  
RAID-Net: Region-Aware Image Deblurring Network Under Guidance of the Image Blur Formulation 期刊论文
IEEE ACCESS, 2022, 卷号: 10, 页码: 83940-83948
作者:  Liao, Lianjun;  Zhang, Zihao;  Xia, Shihong
收藏  |  浏览/下载：27/0  |  提交时间：2022/12/07
Image restoration  Cameras  Convolutional neural networks  Task analysis  Adaptation models  Image edge detection  Trajectory  Attention mechanism  graph neural network  graph reasoning network  image deblur  image processing  
Graph Jigsaw Learning for Cartoon Face Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 3961-3972
作者:  Li, Yong;  Lao, Lingjie;  Cui, Zhen;  Shan, Shiguang;  Yang, Jian
收藏  |  浏览/下载：27/0  |  提交时间：2022/12/07
Face recognition  Shape  Training  Image color analysis  Layout  Convolutional neural networks  Task analysis  Cartoon face recognition  jigsaw solving  graph convolutional network  self-supervised learning