14.5.9.7.1 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.Z.[Bei-Zhan], 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

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

Liu, Y.S.[Yong-Sheng], Chen, W.Y.[Wen-Yu], Qu, H.[Hong], Mahmud, S.M.H.[S.M. Hasan], Miao, K.B.[Ke-Bin],
Weakly supervised image classification and pointwise localization with graph convolutional networks,
PR(109), 2021, pp. 107596.
Elsevier DOI 2009
Deep learning, Learning systems, Convolutional neural networks, Predictive models, Image classification, Graph theory BibRef

Wang, H., Zou, Y., Chong, D., Wang, W.,
Modeling Label Dependencies for Audio Tagging With Graph Convolutional Network,
SPLetters(27), 2020, pp. 1560-1564.
IEEE DOI 2009
Tagging, Acoustics, Spectrogram, Training, Convolution, Symmetric matrices, Probability, Audio tagging, label dependencies, representation learning BibRef

Chang, J.L.[Jian-Long], Wang, L.F.[Ling-Feng], Meng, G.F.[Gao-Feng], Zhang, Q.[Qi], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Local-Aggregation Graph Networks,
PAMI(42), No. 11, November 2020, pp. 2874-2886.
IEEE DOI 2010
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Deep Adaptive Image Clustering,
ICCV17(5880-5888)
IEEE DOI 1802
Convolution, Neural networks, Message passing, Laplace equations, Aggregates, Pattern recognition, Function approximation, non-Euclidean structured signal. feature extraction, image classification, iterative methods, Training BibRef

Liu, Y.B.[Yan-Bei], Wang, Q.[Qi], Wang, X.[Xiao], Zhang, F.[Fang], Geng, L.[Lei], Wu, J.[Jun], Xiao, Z.T.[Zhi-Tao],
Community enhanced graph convolutional networks,
PRL(138), 2020, pp. 462-468.
Elsevier DOI 1806
Graph representation learning, Community structure, Graph convolutional networks BibRef

Li, Q.[Qing], Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
Learning label correlations for multi-label image recognition with graph networks,
PRL(138), 2020, pp. 378-384.
Elsevier DOI 1806
Multi-label image recognition, Graph convolutional networks, Label correlation graph, Sparse correlation constraint BibRef

Ye, J.[Jin], He, J.J.[Jun-Jun], Peng, X.J.[Xiao-Jiang], Wu, W.H.[Wen-Hao], Qiao, Y.[Yu],
Attention-driven Dynamic Graph Convolutional Network for Multi-label Image Recognition,
ECCV20(XXI:649-665).
Springer DOI 2011
BibRef

Bai, S.[Song], Zhang, F.H.[Fei-Hu], Torr, P.H.S.[Philip H.S.],
Hypergraph convolution and hypergraph attention,
PR(110), 2021, pp. 107637.
Elsevier DOI 2011
Graph learning, Hypergraph learning, Graph neural networks, Semi-supervised learning BibRef

Gama, F., Isufi, E., Leus, G., Ribeiro, A.,
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks,
SPMag(37), No. 6, November 2020, pp. 128-138.
IEEE DOI 2011
Convolution, Finite impulse response filters, Autoregressive processes, Network topology, Information filters, Graphical models BibRef

Li, Y.S.[Yan-Sheng], Chen, R.X.[Rui-Xian], Zhang, Y.J.[Yong-Jun], Zhang, M.[Mi], Chen, L.[Ling],
Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Dong, W.[Wei], Wu, J.S.[Jun-Sheng], Bai, Z.W.[Zong-Wen], Hu, Y.Q.[Yao-Qi], Li, W.G.[Wei-Gang], Qiao, W.[Wei], Wozniak, M.[Marcin],
MobileGCN applied to low-dimensional node feature learning,
PR(112), 2021, pp. 107788.
Elsevier DOI 2102
Graph convolutional networks, Affinity-aware encoding, Updater, Depth-wise separable graph convolution, Low-Dimensional node features BibRef

Sun, B., Zhang, H., Wu, Z., Zhang, Y., Li, T.,
Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification,
SPLetters(28), 2021, pp. 219-223.
IEEE DOI 2102
Convolution, Electroencephalography, Spatiotemporal phenomena, Feature extraction, Electrodes, Task analysis, Adaptive systems, spatiotemporal structure BibRef

Pu, S.L.[Sheng-Liang], Wu, Y.F.[Yuan-Feng], Sun, X.[Xu], Sun, X.T.[Xiao-Tong],
Hyperspectral Image Classification with Localized Graph Convolutional Filtering,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Jiang, J.J.[Jun-Jie], He, Z.X.[Zai-Xing], Zhang, S.Y.[Shu-You], Zhao, X.Y.[Xin-Yue], Tan, J.R.[Jian-Rong],
Learning to transfer focus of graph neural network for scene graph parsing,
PR(112), 2021, pp. 107707.
Elsevier DOI 2102
Semantic relationship, Graphical focus, Scene graph, Class imbalance, Image understanding BibRef

Ruiz, L.[Luana], Gama, F.[Fernando], Ribeiro, A.[Alejandro],
Graph Neural Networks: Architectures, Stability, and Transferability,
PIEEE(109), No. 5, May 2021, pp. 660-682.
IEEE DOI 2105
Training, Stability analysis, Convolution, Neural networks, Transforms, Strain, Probability distribution, Equivariance, transferability BibRef

Nie, W.Z.[Wei-Zhi], Ren, M.J.[Min-Jie], Liu, A.A.[An-An], Mao, Z.D.[Zhen-Dong], Nie, J.[Jie],
M-GCN: Multi-Branch Graph Convolution Network for 2D Image-based on 3D Model Retrieval,
MultMed(23), 2021, pp. 1962-1976.
IEEE DOI 2107
Solid modeling, Computational modeling, Visualization, multiple graphs BibRef

Manessi, F.[Franco], Rozza, A.[Alessandro],
Graph-based neural network models with multiple self-supervised auxiliary tasks,
PRL(148), 2021, pp. 15-21.
Elsevier DOI 2107
Graph neural networks, Self-supervised learning, Multi-task learning, Graph convolutional networks, Semi-supervised learning BibRef

Martineau, M.[Maxime], Raveaux, R.[Romain], Conte, D.[Donatello], Venturini, G.[Gilles],
Graph matching as a graph convolution operator for graph neural networks,
PRL(149), 2021, pp. 59-66.
Elsevier DOI 2108
BibRef

Wang, W.[Wei], Gao, J.Y.[Jun-Yu], Yang, X.[Xiaoshan], Xu, C.S.[Chang-Sheng],
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval,
MultMed(23), 2021, pp. 2386-2397.
IEEE DOI 2108
Feature extraction, Encoding, Task analysis, Semantics, Data models, Cognition, Focusing, Video-text retrieval, graph neural network, coarse-to-fine strategy BibRef

Cao, P.P.[Ping-Ping], Chen, P.[Pengpeng], Niu, Q.[Qiang],
Multi-label image recognition with two-stream dynamic graph convolution networks,
IVC(113), 2021, pp. 104238.
Elsevier DOI 2108
Multi-label image recognition, Two streams, Reconstructing graph feature nodes, Dynamic graph convolution networks BibRef

Zhang, Z.[Zhong], Zhang, H.J.[Hai-Jia], Liu, S.[Shuang], Xie, Y.[Yuan], Durrani, T.S.[Tariq S.],
Part-guided graph convolution networks for person re-identification,
PR(120), 2021, pp. 108155.
Elsevier DOI 2109
Person re-identification, Graph convolution network BibRef

Wang, J.[Jie], Liang, J.[Jiye], Yao, K.X.[Kai-Xuan], Liang, J.Q.[Jian-Qing], Wang, D.H.[Dian-Hui],
Graph convolutional autoencoders with co-learning of graph structure and node attributes,
PR(121), 2022, pp. 108215.
Elsevier DOI 2109
Graph representation learning, Graph convolutional autoencoders, Graph filter BibRef

Jiang, B.[Bo], Sun, P.F.[Peng-Fei], Luo, B.[Bin],
GLMNet: Graph learning-matching convolutional networks for feature matching,
PR(121), 2022, pp. 108167.
Elsevier DOI 2109
Graph matching, Graph learning, Graph convolutional network, Laplacian sharpening BibRef

Mesgaran, M.[Mahsa], Hamza, A.B.[A. Ben],
Anisotropic Graph Convolutional Network for Semi-Supervised Learning,
MultMed(23), 2021, pp. 3931-3942.
IEEE DOI 2112
Convolution, Task analysis, Laplace equations, Smoothing methods, Semisupervised learning, Anisotropic magnetoresistance, Geometry, classification BibRef

Abadal, S.[Sergi], Jain, A.[Akshay], Guirado, R.[Robert], Lopez-Alonso, J.[Jorge], Alarcon, E.[Eduard],
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators,
Surveys(54), No. 9, October 2021, pp. xx-yy.
DOI Link 2112
Survey, Graph Neural Networks. Graph neural networks, GNN algorithms, graph embeddings, accelerators BibRef

Tiezzi, M.[Matteo], Marra, G.[Giuseppe], Melacci, S.[Stefano], Maggini, M.[Marco],
Deep Constraint-Based Propagation in Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 727-739.
IEEE DOI 2201
Optimization, Computational modeling, Training, Graph neural networks, Data models, Biological neural networks, lagrangian optimization BibRef

Ciano, G.[Giorgio], Rossi, A.[Alberto], Bianchini, M.[Monica], Scarselli, F.[Franco],
On Inductive-Transductive Learning With Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 758-769.
IEEE DOI 2201
Neural networks, Computational modeling, Training, Encoding, Graph neural networks, Topology, Diffusion processes, inductive learning BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Jiao, Y.H.[Yu-Hang], Rossi, L.[Luca], Hancock, E.R.[Edwin R.],
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification,
PAMI(44), No. 2, February 2022, pp. 783-798.
IEEE DOI 2201
Convolution, Adaptation models, Transforms, Convolutional neural networks, Standards, Feature extraction, backtrackless walk BibRef

Kim, Y.[Youngeun], Hong, S.[Sungeun],
Adaptive Graph Adversarial Networks for Partial Domain Adaptation,
CirSysVideo(32), No. 1, January 2022, pp. 172-182.
IEEE DOI 2201
Handheld computers, Training, Standards, Task analysis, Deep learning, Convolution, Adaptive systems, graph convolutional networks BibRef

Kenning, M.[Michael], Deng, J.J.[Jing-Jing], Edwards, M.[Michael], Xie, X.H.[Xiang-Hua],
A directed graph convolutional neural network for edge-structured signals in link-fault detection,
PRL(153), 2022, pp. 100-106.
Elsevier DOI 2201
Graph deep learning, Datacenter, Directed graph, Edge signals, Graph edge learning, Linegraphs, Directed linegraphs, Graph convolution BibRef

Yang, Z.Q.[Zi-Qing], Han, S.[Shoudong], Zhao, J.[Jun],
Poisson kernel: Avoiding self-smoothing in graph convolutional networks,
PR(124), 2022, pp. 108443.
Elsevier DOI 2203
Graph convolutional kernel, Graph convolutional network, Graph neural network, Graph structure, Self-smoothing BibRef

Zheng, R.G.[Rui-Gang], Chen, W.[Weifu], Feng, G.[Guocan],
Semi-supervised node classification via adaptive graph smoothing networks,
PR(124), 2022, pp. 108492.
Elsevier DOI 2203
Adaptive graph smoothing networks, Graph convolutional networks, Semi-supervised learning, Graph node classification BibRef

Ding, J.Y.[Jing-Yi], Cheng, R.[Ruohui], Song, J.[Jian], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng], Wu, J.[Jianshe],
Graph label prediction based on local structure characteristics representation,
PR(125), 2022, pp. 108525.
Elsevier DOI 2203
Graph classification, Graph neural network, Betweenness centrality node, Feature fusion, Characteristics representation BibRef

Chen, Y.C.[Yu-Chi], Lai, K.T.[Kuan-Ting], Liu, D.[Dong], Chen, M.S.[Ming-Syan],
TAGNet: Triplet-Attention Graph Networks for Hashtag Recommendation,
CirSysVideo(32), No. 3, March 2022, pp. 1148-1159.
IEEE DOI 2203
Feature extraction, Visualization, Social networking (online), Correlation, Convolution, Fuses, Blogs, Hashtag recommendation, attention mechanism BibRef

Dong, X.F.[Xin-Feng], Liu, L.[Li], Zhu, L.[Lei], Nie, L.Q.[Li-Qiang], Zhang, H.X.[Hua-Xiang],
Adversarial Graph Convolutional Network for Cross-Modal Retrieval,
CirSysVideo(32), No. 3, March 2022, pp. 1634-1645.
IEEE DOI 2203
Semantics, Feature extraction, Task analysis, Generative adversarial networks, Correlation, Generators BibRef

Zhao, Y.[Yue], Zhang, L.M.[Ling-Ming], Liu, Y.[Yang], Meng, D.Y.[De-Yu], Cui, Z.M.[Zhi-Ming], Gao, C.Q.[Chen-Qiang], Gao, X.[Xinbo], Lian, C.F.[Chun-Feng], Shen, D.G.[Ding-Gang],
Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation,
MedImg(41), No. 4, April 2022, pp. 826-835.
IEEE DOI 2204
BibRef
Earlier: A2, A1, A4, A5, A6, A7, A8, A9, Only:
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation,
CVPR21(6695-6704)
IEEE DOI 2111
Image segmentation, Teeth, Shape, Task analysis, Dentistry, Feature extraction, Intra-oral scanner image segmentation, graph convolutional network. Solid modeling, Surgery, Predictive models BibRef

Gao, Y.[Yue], Zhang, Z.Z.[Zi-Zhao], Lin, H.J.[Hao-Jie], Zhao, X.B.[Xi-Bin], Du, S.Y.[Shao-Yi], Zou, C.Q.[Chang-Qing],
Hypergraph Learning: Methods and Practices,
PAMI(44), No. 5, May 2022, pp. 2548-2566.
IEEE DOI 2204
Learning systems, Correlation, Data models, Laplace equations, Brain modeling, Task analysis, Hypergraph learning, classification and clustering BibRef

Tinega, H.C.[Haron C.], Chen, E.[Enqing], Ma, L.[Long], Nyasaka, D.O.[Divinah O.], Mariita, R.M.[Richard M.],
HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yao, K.X.[Kai-Xuan], Liang, J.[Jiye], Liang, J.Q.[Jian-Qing], Li, M.[Ming], Cao, F.L.[Fei-Long],
Multi-view graph convolutional networks with attention mechanism,
AI(307), 2022, pp. 103708.
Elsevier DOI 2204
Graph neural networks, Multi-view learning, Attention mechanism, Semi-supervised learning BibRef

Kan, S.C.[Shi-Chao], Cen, Y.G.[Yi-Gang], Li, Y.[Yang], Vladimir, M.[Mladenovic], He, Z.H.[Zhi-Hai],
Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval,
IP(31), 2022, pp. 2988-3003.
IEEE DOI 2205
Correlation, Graph neural networks, Measurement, Semantics, Image retrieval, Training, Visualization, Deep feature embedding BibRef

Zhang, L.[Li], Song, H.[Heda], Aletras, N.[Nikolaos], Lu, H.P.[Hai-Ping],
Node-Feature Convolution for Graph Convolutional Networks,
PR(128), 2022, pp. 108661.
Elsevier DOI 2205
Graph, Representation learning, Graph convolutional networks, Convolutional neural networks BibRef

Liang, H.J.[Hao-Jian], Wang, S.H.[Shao-Hua], Li, H.[Huilai], Ye, H.[Huichun], Zhong, Y.[Yang],
A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network,
IJGI(11), No. 5, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Bianchi, F.M.[Filippo Maria], Grattarola, D.[Daniele], Livi, L.[Lorenzo], Alippi, C.[Cesare],
Graph Neural Networks With Convolutional ARMA Filters,
PAMI(44), No. 7, July 2022, pp. 3496-3507.
IEEE DOI 2206
Convolution, Laplace equations, Task analysis, Graph neural networks, Chebyshev approximation, graph signal processing BibRef

Jing, H.Y.[Hao-Yu], Wang, Y.Y.[Yuan-Yuan], Du, Z.H.[Zhen-Hong], Zhang, F.[Feng],
Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Thang, D.C.[Duong Chi], Dat, H.T.[Hoang Thanh], Tam, N.T.[Nguyen Thanh], Jo, J.[Jun], Hung, N.Q.V.[Nguyen Quoc Viet], Aberer, K.[Karl],
Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks,
PRL(159), 2022, pp. 46-53.
Elsevier DOI 2206
graph neural networks, transferability BibRef

Huang, Y.[Yan], Zhou, X.[Xiao], Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Kang, J.[Jian], Tang, S.[Shiyang], Chen, Z.[Zhanye], Hong, W.[Wei],
Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Jiang, B.[Bo], Wang, B.B.[Bei-Bei], Tang, J.[Jin], Luo, B.[Bin],
GeCNs: Graph Elastic Convolutional Networks for Data Representation,
PAMI(44), No. 9, September 2022, pp. 4935-4947.
IEEE DOI 2208
Convolution, Task analysis, Optimization, Supervised learning, Machine learning, Training, Sparse matrices, graph representation BibRef

Zhao, Z.N.[Zhi-Neng], Liu, Q.F.[Qi-Fan], Cao, W.M.[Wen-Ming], Lian, D.L.[De-Liang], He, Z.H.[Zhi-Hai],
Self-guided information for few-shot classification,
PR(131), 2022, pp. 108880.
Elsevier DOI 2208
Few-shot classification, Graph convolution network, Self-guided information BibRef

Guan, W.[Weili], Wen, H.[Haokun], Song, X.[Xuemeng], Wang, C.[Chun], Yeh, C.H.[Chung-Hsing], Chang, X.J.[Xiao-Jun], Nie, L.Q.[Li-Qiang],
Partially Supervised Compatibility Modeling,
IP(31), 2022, pp. 4733-4745.
IEEE DOI 2208
Visualization, Representation learning, Graph neural networks, Task analysis, Semantics, Image color analysis, Data models, graph convolutional network BibRef

Gao, H.Y.[Hong-Yang], Ji, S.W.[Shui-Wang],
Graph U-Nets,
PAMI(44), No. 9, September 2022, pp. 4948-4960.
IEEE DOI 2208
Task analysis, Topology, Feature extraction, Computer architecture, Neural networks, Logic gates, Lattices, Graph neural networks, U-Net BibRef

Duan, Y.J.[Yi-Jun], Liu, X.[Xin], Jatowt, A.[Adam], Yu, H.T.[Hai-Tao], Lynden, S.[Steven], Kim, K.S.[Kyoung-Sook], Matono, A.[Akiyoshi],
Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional Network,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Duan, Y.J.[Yi-Jun], Liu, X.[Xin], Jatowt, A.[Adam], Yu, H.T.[Hai-Tao], Lynden, S.[Steven], Kim, K.S.[Kyoung-Sook], Matono, A.[Akiyoshi],
SORAG: Synthetic Data Over-Sampling Strategy on Multi-Label Graphs,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Wang, R.Z.[Run-Zhong], Yan, J.C.[Jun-Chi], Yang, X.K.[Xiao-Kang],
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching,
PAMI(44), No. 9, September 2022, pp. 5261-5279.
IEEE DOI 2208
Pattern matching, Tensors, Splines (mathematics), Feature extraction, Peer-to-peer computing, Optimization, graph neural networks BibRef

Ma, X.Q.[Xue-Qi], Liu, W.F.[Wei-Feng], Tian, Q.[Qi], Gao, Y.[Yue],
Learning Representation on Optimized High-Order Manifold for Visual Classification,
MultMed(24), 2022, pp. 3989-4001.
IEEE DOI 2208
Manifolds, Laplace equations, Correlation, Task analysis, Visualization, Shape, High-order manifold, hypergraph, visual classification BibRef

Tian, Y.[Yu], Sun, X.[Xian], Niu, R.G.[Rui-Gang], Yu, H.F.[Hong-Feng], Zhu, Z.C.[Zi-Cong], Wang, P.[Peijin], Fu, K.[Kun],
Fully-weighted HGNN: Learning efficient non-local relations with hypergraph in aerial imagery,
PandRS(191), 2022, pp. 263-276.
Elsevier DOI 2208
Aerial imagery, Hypergraph neural networks, Fully-weighted Hypergraph Neural Network (fully-weighted HGNN), Hypergraph Convolutional Feature Pyramid Networks (hyper-FPN) BibRef

Liu, X.[Xue], Sun, D.[Dan], Wei, W.[Wei],
Alleviating the over-smoothing of graph neural computing by a data augmentation strategy with entropy preservation,
PR(132), 2022, pp. 108951.
Elsevier DOI 2209
Graph representation, Graph convolutional networks, Information theory, Graph entropy BibRef


Wu, H.Y.[Hong-Yan], Guo, H.Y.[Hai-Yun], Miao, Q.H.[Qing-Hai], Huang, M.[Min], Wang, J.Q.[Jin-Qiao],
Graph Neural Networks Based Multi-granularity Feature Representation Learning for Fine-Grained Visual Categorization,
MMMod22(II:230-242).
Springer DOI 2203
BibRef

Zhao, G.M.[Gang-Ming], Ge, W.F.[Wei-Feng], Yu, Y.Z.[Yi-Zhou],
GraphFPN: Graph Feature Pyramid Network for Object Detection,
ICCV21(2743-2752)
IEEE DOI 2203
Representation learning, Image segmentation, Network topology, Object detection, Feature extraction, Graph neural networks, grouping and shape BibRef

Xing, Y.F.[Yi-Fan], He, T.[Tong], Xiao, T.J.[Tian-Jun], Wang, Y.X.[Yong-Xin], Xiong, Y.J.[Yuan-Jun], Xia, W.[Wei], Wipf, D.[David], Zhang, Z.[Zheng], Soatto, S.[Stefano],
Learning Hierarchical Graph Neural Networks for Image Clustering,
ICCV21(3447-3457)
IEEE DOI 2203
Training, Couplings, Computational modeling, Predictive models, Prediction algorithms, Graph neural networks, Faces, Recognition and classification BibRef

Liu, N.[Nian], Zhao, W.[Wangbo], Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Shao, L.[Ling],
Light Field Saliency Detection with Dual Local Graph Learning and Reciprocative Guidance,
ICCV21(4692-4701)
IEEE DOI 2203
Fuses, Convolution, Computational modeling, Object detection, Light fields, Graph neural networks, Scene analysis and understanding BibRef

Wang, T.T.[Tian-Tian], Liu, S.[Sifei], Tian, Y.[Yapeng], Li, K.[Kai], Yang, M.H.[Ming-Hsuan],
Video Matting via Consistency-Regularized Graph Neural Networks,
ICCV21(4882-4891)
IEEE DOI 2203
Training, Adaptation models, Computational modeling, Coherence, Predictive models, Graph neural networks, grouping and shape BibRef

Jing, Y.C.[Yong-Cheng], Yang, Y.D.[Yi-Ding], Wang, X.C.[Xin-Chao], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng],
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks,
ICCV21(5281-5290)
IEEE DOI 2203
Visualization, Adaptation models, Network topology, Computational modeling, Aggregates, Transformers, Vision applications and systems BibRef

Li, X.Y.[Xin-Yi], Ling, H.B.[Hai-Bin],
PoGO-Net: Pose Graph Optimization with Graph Neural Networks,
ICCV21(5875-5885)
IEEE DOI 2203
Training, Simultaneous localization and mapping, Pose estimation, Benchmark testing, Cameras, Robustness, Graph neural networks, Vision for robotics and autonomous vehicles BibRef

Chen, H.K.[Hong-Kai], Luo, Z.X.[Zi-Xin], Zhang, J.[Jiahui], Zhou, L.[Lei], Bai, X.[Xuyang], Hu, Z.[Zeyu], Tai, C.L.[Chiew-Lan], Quan, L.[Long],
Learning to Match Features with Seeded Graph Matching Network,
ICCV21(6281-6290)
IEEE DOI 2203
Costs, Filtering, Message passing, Image matching, Computer network reliability, Graph neural networks, Stereo, Low-level and physics-based vision BibRef

Yu, Z.J.[Zi-Jian], Li, X.[Xuhui], Huang, H.J.[Hui-Juan], Zheng, W.[Wen], Chen, L.[Li],
Cascade Image Matting with Deformable Graph Refinement,
ICCV21(7147-7156)
IEEE DOI 2203
Image resolution, Estimation, Computer architecture, Graph neural networks, Convolutional neural networks, BibRef

Arnab, A.[Anurag], Sun, C.[Chen], Schmid, C.[Cordelia],
Unified Graph Structured Models for Video Understanding,
ICCV21(8097-8106)
IEEE DOI 2203
Computational modeling, Message passing, Genomics, Cognition, Graph neural networks, Task analysis, Action and behavior recognition BibRef

Fang, P.F.[Peng-Fei], Harandi, M.[Mehrtash], Petersson, L.[Lars],
Kernel Methods in Hyperbolic Spaces,
ICCV21(10645-10654)
IEEE DOI 2203
Geometry, Machine learning, Hilbert space, Natural language processing, Graph neural networks, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zeng, A.[Ailing], Sun, X.[Xiao], Yang, L.[Lei], Zhao, N.[Nanxuan], Liu, M.[Minhao], Xu, Q.[Qiang],
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation,
ICCV21(11416-11425)
IEEE DOI 2203
Representation learning, Deep learning, Codes, Pose estimation, Graph neural networks, Gestures and body pose, Representation learning BibRef

Zhang, C.[Cheng], Cui, Z.P.[Zhao-Peng], Chen, C.[Cai], Liu, S.C.[Shuai-Cheng], Zeng, B.[Bing], Bao, H.J.[Hu-Jun], Zhang, Y.[Yinda],
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization,
ICCV21(12612-12621)
IEEE DOI 2203
Shape, Layout, Semantics, Predictive models, Linear programming, Graph neural networks, 3D from a single image and shape-from-x, Detection and localization in 2D and 3D BibRef

Li, Y.[Yao], Fu, X.[Xueyang], Zha, Z.J.[Zheng-Jun],
Cross-Patch Graph Convolutional Network for Image Denoising,
ICCV21(4631-4640)
IEEE DOI 2203
Training, Image resolution, Pipelines, Noise reduction, Training data, Robustness, Hardware, Computational photography BibRef

Huang, H.M.[Hui-Min], Lin, L.[Lanfen], Zhang, Y.[Yue], Xu, Y.Y.[Ying-Ying], Zheng, J.[Jing], Mao, X.W.[Xiong-Wei], Qian, X.H.[Xiao-Han], Peng, Z.[Zhiyi], Zhou, J.Y.[Jian-Ying], Chen, Y.W.[Yen-Wei], Tong, R.F.[Ruo-Feng],
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network with Bilateral Graph Convolution,
ICCV21(7366-7375)
IEEE DOI 2203
Image segmentation, Shape, Convolution, Semantics, Semisupervised learning, Multitasking, Feature extraction, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Sofianos, T.[Theodoros], Sampieri, A.[Alessio], Franco, L.[Luca], Galasso, F.[Fabio],
Space-Time-Separable Graph Convolutional Network for Pose Forecasting,
ICCV21(11189-11198)
IEEE DOI 2203
Convolutional codes, Correlation, Computational modeling, Time series analysis, Dynamics, Kinematics, Gestures and body pose, Motion and tracking BibRef

Dang, L.W.[Ling-Wei], Nie, Y.W.[Yong-Wei], Long, C.J.[Cheng-Jiang], Zhang, Q.[Qing], Li, G.Q.[Gui-Qing],
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction,
ICCV21(11447-11456)
IEEE DOI 2203
Convolutional codes, Convolution, Dynamics, Benchmark testing, Feature extraction, History, Gestures and body pose, Machine learning architectures and formulations BibRef

Yew, Z.J.[Zi Jian], Lee, G.H.[Gim Hee],
Learning Iterative Robust Transformation Synchronization,
3DV21(1206-1215)
IEEE DOI 2201
Analytical models, Message passing, Pipelines, Graph neural networks, Synchronization, Iterative methods, registration BibRef

Sahbi, H.[Hichem],
Learning Laplacians in Chebyshev Graph Convolutional Networks,
DLGC21(2064-2075)
IEEE DOI 2112
Training, Laplace equations, Convolution, Databases, Neural networks, Chebyshev approximation, Skeleton BibRef

Potter, K.[Kevin], Sleder, S.[Steven], Smith, M.[Matthew], Perera, S.[Shehan], Yilmaz, A.[Alper], Tencer, J.[John],
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks,
GSP-CV21(904-912)
IEEE DOI 2112
Image segmentation, Image coding, Shape, Convolution, Image edge detection, Supervised learning BibRef

Bahri, M.[Mehdi], Bahl, G.[Gaétan], Zafeiriou, S.P.[Stefanos P.],
Binary Graph Neural Networks,
CVPR21(9487-9496)
IEEE DOI 2111
Training, Schedules, Heuristic algorithms, Computational modeling, Memory management, Process control, Benchmark testing BibRef

Caramalau, R.[Razvan], Bhattarai, B.[Binod], Kim, T.K.[Tae-Kyun],
Sequential Graph Convolutional Network for Active Learning,
CVPR21(9578-9587)
IEEE DOI 2111
Convolution, Image edge detection, Pose estimation, Benchmark testing, Pattern recognition, Task analysis BibRef

Yang, X.[Xu], Deng, C.[Cheng], Dang, Z.Y.[Zhi-Yuan], Wei, K.[Kun], Yan, J.C.[Jun-Chi],
Self-SAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network,
CVPR21(16770-16779)
IEEE DOI 2111
Deep learning, Convolution, Computational modeling, Semantics, Benchmark testing, Feature extraction BibRef

Dai, J.[Jindou], Wu, Y.W.[Yu-Wei], Gao, Z.[Zhi], Jia, Y.D.[Yun-De],
A Hyperbolic-to-Hyperbolic Graph Convolutional Network,
CVPR21(154-163)
IEEE DOI 2111
Manifolds, Geometry, Convolution, Distortion, Pattern recognition, Task analysis BibRef

Wang, J.F.[Jun-Fu], Wang, Y.H.[Yun-Hong], Yang, Z.[Zhen], Yang, L.[Liang], Guo, Y.F.[Yuan-Fang],
Bi-GCN: Binary Graph Convolutional Network,
CVPR21(1561-1570)
IEEE DOI 2111
Memory management, Loading, Graph neural networks, Pattern recognition, Task analysis BibRef

Jing, Y.C.[Yong-Cheng], Yang, Y.D.[Yi-Ding], Wang, X.C.[Xin-Chao], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng],
Amalgamating Knowledge from Heterogeneous Graph Neural Networks,
CVPR21(15704-15713)
IEEE DOI 2111
Knowledge engineering, Convolutional codes, Annotations, Semantics, Computer architecture, Graph neural networks BibRef

Miyata, M.[Masaki], Shiraki, K.[Katsutoshi], Minoura, H.[Hiroaki], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Relational Subgraph for Graph-based Path Prediction,
MVA21(1-5)
DOI Link 2109
Prediction methods, Feature extraction BibRef

Hoang, N.T., Maehara, T.[Takanori], Murata, T.[Tsuyoshi],
Revisiting Graph Neural Networks: Graph Filtering Perspective,
ICPR21(8376-8383)
IEEE DOI 2105
Convolutional codes, Analytical models, Filtering, Convolution, Graph neural networks BibRef

Lyu, Y.C.[Ye-Cheng], Li, M.[Ming], Huang, X.M.[Xin-Ming], Guler, U.[Ulkuhan], Schaumont, P.[Patrick], Zhang, Z.[Ziming],
TreeRNN: Topology-Preserving Deep Graph Embedding and Learning,
ICPR21(7493-7499)
IEEE DOI 2105
NN learning of grap structues. Image segmentation, Recurrent neural networks, Convolution, Message passing, Image representation BibRef

Dominguez, M.[Miguel], Ptucha, R.[Raymond],
Directional Graph Networks with Hard Weight Assignments,
ICPR21(7439-7446)
IEEE DOI 2105
Convolution, Computational modeling, Neural networks, Robot sensing systems, Computational efficiency, Sensors BibRef

Zhang, Y.H.[Yu-Hang], Ren, H.S.[Hong-Shuai], Ye, J.X.[Jie-Xia], Gao, X.T.[Xi-Tong], Wang, Y.[Yang], Ye, K.J.[Ke-Jiang], Xu, C.Z.[Cheng-Zhong],
AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network,
ICPR21(5130-5136)
IEEE DOI 2105
Training, Correlation, Convolution, Heuristic algorithms, Focusing, Reinforcement learning, Search problems, Node Information Entropy BibRef

Sahbi, H.[Hichem],
Kernel-based Graph Convolutional Networks,
ICPR21(4887-4894)
IEEE DOI 2105
Training, Convolution, Image color analysis, Training data, Hilbert space, Kernel BibRef

Li, Z.X.[Zhi-Xin], Sun, Y.[Yaru], Tang, S.[Suqin], Zhang, C.L.[Can-Long], Ma, H.F.[Hui-Fang],
Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction,
ICPR21(946-953)
IEEE DOI 2105
Convolution, Aggregates, Semantics, Reinforcement learning, Information representation, Feature extraction, Cognition BibRef

Deng, J.[Jiehui], Wan, S.[Sheng], Wang, X.[Xiang], Tu, E.[Enmei], Huang, X.L.[Xiao-Lin], Yang, J.[Jie], Gong, C.[Chen],
Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks,
ICPR21(9988-9995)
IEEE DOI 2105
Deep learning, Fuses, Estimation, Switches, Pattern recognition BibRef

Tian, Y.X.[Yu-Xing], Liu, Z.[Zheng], Liu, W.[Weiding], Zhang, Z.[Zeyu], Qu, Y.[Yanwen],
What nodes vote to? Graph classification without readout phase,
ICPR21(8439-8445)
IEEE DOI 2105
Message passing, Logic gates, Benchmark testing, Feature extraction, Graph neural networks, Decoding, graph neural networks BibRef

Wang, C.[Chen], Deng, C.Y.[Cheng-Yuan],
On the Global Self-attention Mechanism for Graph Convolutional Networks,
ICPR21(8531-8538)
IEEE DOI 2105
Benchmark testing, Pattern recognition, Convolutional neural networks, Task analysis BibRef

Yang, L.[Lei], Huang, Q.Q.[Qing-Qiu], Huang, H.Y.[Huai-Yi], Xu, L.N.[Lin-Ning], Lin, D.[Dahua],
Learn to Propagate Reliably on Noisy Affinity Graphs,
ECCV20(XV:447-464).
Springer DOI 2011
exploiting unlabeled data. BibRef

Park, H., Jeong, M., Kim, Y., Kim, C.,
Self-Training Of Graph Neural Networks Using Similarity Reference For Robust Training With Noisy Labels,
ICIP20(1951-1955)
IEEE DOI 2011
Training, Sampling methods, Noise measurement, Feature extraction, Training data, Indexes, Data mining, Noisy label, sampling method, graph-based CNN. BibRef

Adaloglou, N.[Nikolas], Vretos, N.[Nicholas], Daras, P.[Petros],
Multi-view Adaptive Graph Convolutions for Graph Classification,
ECCV20(XXVI:398-414).
Springer DOI 2011
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Zhang, X.K.[Xi-Kun], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
On Dropping Clusters to Regularize Graph Convolutional Neural Networks,
ECCV20(XXI:245-260).
Springer DOI 2011
BibRef

Korban, M.[Matthew], Li, X.[Xin],
Ddgcn: A Dynamic Directed Graph Convolutional Network for Action Recognition,
ECCV20(XX:761-776).
Springer DOI 2011
BibRef

Yu, C.Q.[Chang-Qian], Liu, Y.F.[Yi-Fan], Gao, C.X.[Chang-Xin], Shen, C.H.[Chun-Hua], Sang, N.[Nong],
Representative Graph Neural Network,
ECCV20(VII:379-396).
Springer DOI 2011
BibRef

Iscen, A.[Ahmet], Tolias, G.[Giorgos], Avrithis, Y.[Yannis], Chum, O.[Ondrej], Schmid, C.[Cordelia],
Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels,
ECCV20(X:286-302).
Springer DOI 2011
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

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

Zhang, K.H.[Kai-Hua], Li, T.P.[Teng-Peng], Shen, S.W.[Shi-Wen], Liu, B.[Bo], Chen, J.[Jin], Liu, Q.S.[Qing-Shan],
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

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

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


Last update:Sep 28, 2022 at 16:10:08