14.5.8.6.1 Graph Convolutional Neural Networks

Chapter Contents (Back)
Convolutional Neural Networks. Neural Networks. Graph Convolutional Neural Networks.

Fu, S.[Sichao], Liu, W.F.[Wei-Feng], Li, S.Y.[Shu-Ying], Zhou, Y.C.[Yi-Cong],
Two-order graph convolutional networks for semi-supervised classification,
IET-IPR(13), No. 14, 12 December 2019, pp. 2763-2771.
DOI Link 1912
BibRef

Zhang, Z.H.[Zhi-Hong], Chen, D.D.[Dong-Dong], Wang, J.J.[Jian-Jia], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Quantum-based subgraph convolutional neural networks,
PR(88), 2019, pp. 38-49.
Elsevier DOI 1901
Graph convolutional neural networks, Spatial construction, Quantum walks, Subgraph BibRef

Xu, C.Y.[Chuan-Yu], Wang, D.[Dong], Zhang, Z.H.[Zhi-Hong], Wang, B.[Beizhan], Zhou, D.[Da], Ren, G.J.[Gui-Jun], Bai, L.[Lu], Cui, L.X.[Li-Xin], Hancock, E.R.[Edwin R.],
Depth-based Subgraph Convolutional Neural Networks,
ICPR18(1024-1029)
IEEE DOI 1812
Convolution, Feature extraction, Convolutional neural networks, Standards, Task analysis, Data mining, Laplace equations BibRef

Zhang, Z.H.[Zhi-Hong], Chen, D.D.[Dong-Dong], Wang, Z.[Zeli], Li, H.[Heng], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Depth-based subgraph convolutional auto-encoder for network representation learning,
PR(90), 2019, pp. 363-376.
Elsevier DOI 1903
Graph based CNN style learning. Network representation learning, Graph convolutional neural network, Node classification BibRef

Chen, Y.X.[Yu-Xin], Ma, G.[Gaoqun], Yuan, C.F.[Chun-Feng], Li, B.[Bing], Zhang, H.[Hui], Wang, F.[Fangshi], Hu, W.M.[Wei-Ming],
Graph convolutional network with structure pooling and joint-wise channel attention for action recognition,
PR(103), 2020, pp. 107321.
Elsevier DOI 2005
Graph convolutional network, Structure graph pooling, Joint-wise channel attention BibRef

Wan, S.[Sheng], Gong, C.[Chen], Zhong, P.[Ping], Du, B.[Bo], Zhang, L.F.[Le-Fei], Yang, J.[Jian],
Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification,
GeoRS(58), No. 5, May 2020, pp. 3162-3177.
IEEE DOI 2005
Hyperspectral imaging, Convolution, Feature extraction, Kernel, Support vector machines, Training, Dynamic graph, multiscale information BibRef

Luo, Y.[Yawei], Ji, R.R.[Rong-Rong], Guan, T.[Tao], Yu, J.Q.[Jun-Qing], Liu, P.[Ping], Yang, Y.[Yi],
Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning,
PR(106), 2020, pp. 107451.
Elsevier DOI 2006
Teacher-student models, Self-ensemble learning, Graph convolutional networks, Semi-supervised learning BibRef

Wu, J.X.[Jia-Xin], Zhong, S.H.[Sheng-Hua], Liu, Y.[Yan],
Dynamic graph convolutional network for multi-video summarization,
PR(107), 2020, pp. 107382.
Elsevier DOI 2008
Multi-video summarization, Graph convolutional network, Class imbalance problem BibRef

Yu, B.[Bin], Hu, J.Z.[Jin-Zhi], Xie, Y.[Yu], Zhang, C.[Chen], Tang, Z.H.[Zhou-Hua],
Rich heterogeneous information preserving network representation learning,
PR(108), 2020, pp. 107564.
Elsevier DOI 2008
Network representation learning, Heterogeneous information, Autoencoder BibRef


Wang, C.[Chu], Samari, B.[Babak], Kim, V.G.[Vladimir G.], Chaudhuri, S.[Siddhartha], Siddiqi, K.[Kaleem],
Affinity Graph Supervision for Visual Recognition,
CVPR20(8244-8252)
IEEE DOI 2008
Visualization, Training, Task analysis, Manganese, Proposals, Computer architecture, Convolutional neural networks BibRef

Xu, Q.G.[Qian-Geng], Sun, X.D.[Xu-Dong], Wu, C.Y.[Cho-Ying], Wang, P.Q.[Pan-Qu], Neumann, U.[Ulrich],
Grid-GCN for Fast and Scalable Point Cloud Learning,
CVPR20(5660-5669)
IEEE DOI 2008
Computational modeling, Data models, Convolution, Aggregates, Task analysis, Feature extraction BibRef

Wei, X., Yu, R., Sun, J.,
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis,
CVPR20(1847-1856)
IEEE DOI 2008
Shape, Convolution, Feature extraction, Aggregates, Image recognition BibRef

You, Y., Chen, T., Wang, Z., Shen, Y.,
L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks,
CVPR20(2124-2132)
IEEE DOI 2008
Training, Time complexity, Convolution, Memory management, Prediction algorithms, Clustering algorithms BibRef

Yang, Q., Li, C., Dai, W., Zou, J., Qi, G., Xiong, H.,
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification,
CVPR20(4302-4311)
IEEE DOI 2008
Convolution, Kernel, Solid modeling, Distortion, Image quality, Convolutional neural networks BibRef

Lin, J., Yuan, Y., Shao, T., Zhou, K.,
Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks,
CVPR20(5890-5899)
IEEE DOI 2008
Face, Shape, Image reconstruction, Image color analysis, Rendering (computer graphics), Feature extraction BibRef

Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.,
Distilling Knowledge From Graph Convolutional Networks,
CVPR20(7072-7081)
IEEE DOI 2008
Knowledge engineering, Task analysis, Computational modeling, Computer science, Training, Neural networks BibRef

Zhang, K., Li, T., Shen, S., Liu, B., Chen, J., Liu, Q.,
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection,
CVPR20(9047-9056)
IEEE DOI 2008
Feature extraction, Task analysis, Adaptive systems, Decoding, Saliency detection, Visualization, Convolution BibRef

Lin, Z., Huang, S., Wang, Y.F.,
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis,
CVPR20(1797-1806)
IEEE DOI 2008
Convolution, Kernel, Feature extraction, Shape, Task analysis BibRef

Park, J.[Jiwoong], Lee, M.[Minsik], Chang, H.J.[Hyung Jin], Lee, K.[Kyuewang], Choi, J.Y.[Jin Young],
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning,
ICCV19(6518-6527)
IEEE DOI 2004
data visualisation, decoding, encoding, graph theory, image representation, learning (artificial intelligence), BibRef

Mosella-Montoro, A., Ruiz-Hidalgo, J.,
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification,
GMDL19(4123-4132)
IEEE DOI 2004
computational geometry, convolutional neural nets, feature extraction, image classification, image colour analysis, agc BibRef

Sun, H.L.[Hao-Liang], Zhen, X.T.[Xian-Tong], Yin, Y.L.[Yi-Long],
Learning the Set Graphs: Image-Set Classification Using Sparse Graph Convolutional Networks,
ICIP19(4554-4558)
IEEE DOI 1910
Set graph learning, Graph convolutional network, l1,2-Norm, Image-set classification BibRef

Chen, Z.M.[Zhao-Min], Wei, X.S.[Xiu-Shen], Wang, P.[Peng], Guo, Y.[Yanwen],
Multi-Label Image Recognition With Graph Convolutional Networks,
CVPR19(5172-5181).
IEEE DOI 2002
BibRef

Pope, P.E.[Phillip E.], Kolouri, S.[Soheil], Rostami, M.[Mohammad], Martin, C.E.[Charles E.], Hoffmann, H.[Heiko],
Explainability Methods for Graph Convolutional Neural Networks,
CVPR19(10764-10773).
IEEE DOI 2002
BibRef

Zhang, L.[Ling], Zhu, Z.[Zhigang],
Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks,
3DV19(395-404)
IEEE DOI 1911
Task analysis, Feature extraction, Training, Unsupervised learning, Semantics, Graph convolutional neural network BibRef

Litany, O., Bronstein, A., Bronstein, M., Makadia, A.,
Deformable Shape Completion with Graph Convolutional Autoencoders,
CVPR18(1886-1895)
IEEE DOI 1812
Shape, Task analysis, Training, Strain, Neural networks BibRef

Verma, N.[Nitika], Boyer, E.[Edmond], Verbeek, J.[Jakob],
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis,
CVPR18(2598-2606)
IEEE DOI 1812
Shape, Convolution, Standards, Visualization, Neural networks BibRef

Edwards, M.[Michael], Xie, X.H.[Xiang-Hua],
Graph Convolutional Neural Network,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks, Design, Implementation Issues .


Last update:Sep 14, 2020 at 15:32:18