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Liu, W.F.[Wei-Feng],
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Two-order graph convolutional networks for semi-supervised
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IET-IPR(13), No. 14, 12 December 2019, pp. 2763-2771.
DOI Link
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Zhang, Z.H.[Zhi-Hong],
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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
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Xu, C.Y.[Chuan-Yu],
Wang, D.[Dong],
Zhang, Z.H.[Zhi-Hong],
Wang, B.Z.[Bei-Zhan],
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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],
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Depth-based subgraph convolutional auto-encoder for network
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PR(90), 2019, pp. 363-376.
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1903
Graph based CNN style learning.
Network representation learning,
Graph convolutional neural network, Node classification
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Chen, Y.X.[Yu-Xin],
Ma, G.[Gaoqun],
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Graph convolutional network with structure pooling and joint-wise
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PR(103), 2020, pp. 107321.
Elsevier DOI
2005
Graph convolutional network, Structure graph pooling,
Joint-wise channel attention
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Luo, Y.[Yawei],
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Every node counts: Self-ensembling graph convolutional networks for
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PR(106), 2020, pp. 107451.
Elsevier DOI
2006
Teacher-student models, Self-ensemble learning,
Graph convolutional networks, Semi-supervised learning
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Wu, J.X.[Jia-Xin],
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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
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Yu, B.[Bin],
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Rich heterogeneous information preserving network representation
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PR(108), 2020, pp. 107564.
Elsevier DOI
2008
Network representation learning, Heterogeneous information, Autoencoder
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Liu, Y.S.[Yong-Sheng],
Chen, W.Y.[Wen-Yu],
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Weakly supervised image classification and pointwise localization
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PR(109), 2021, pp. 107596.
Elsevier DOI
2009
Deep learning, Learning systems, Convolutional neural networks,
Predictive models, Image classification, Graph theory
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Wang, H.,
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Modeling Label Dependencies for Audio Tagging With Graph
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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],
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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],
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Geng, L.[Lei],
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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],
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Qiao, Y.[Yu],
Peng, Q.A.[Qi-Ang],
Learning label correlations for multi-label image recognition with
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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
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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.S.[Xiao-Shan],
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.P.[Peng-Peng],
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.F.[Wei-Fu],
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.B.[Xin-Bo],
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.K.[Hao-Kun],
Song, X.M.[Xue-Meng],
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,
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
Priebe, C.E.[Carey E.],
Shen, C.[Cencheng],
Huang, N.[Ningyuan],
Chen, T.Y.[Tian-Yi],
A Simple Spectral Failure Mode for Graph Convolutional Networks,
PAMI(44), No. 11, November 2022, pp. 8689-8693.
IEEE DOI
2210
Convolution, Task analysis, Symmetric matrices,
Graph neural networks, Geometry, Training, Training data,
convolutional neural network
BibRef
Zhang, R.[Rui],
Zhang, W.[Wenlin],
Li, P.[Pei],
Li, X.L.[Xue-Long],
Graph Convolution RPCA With Adaptive Graph,
IP(31), 2022, pp. 6062-6071.
IEEE DOI
2210
Principal component analysis, Matrix decomposition, Manifolds,
Sparse matrices, Convolution, Image reconstruction, Robustness,
graph auto-encoder
BibRef
Isufi, E.[Elvin],
Gama, F.[Fernando],
Ribeiro, A.[Alejandro],
EdgeNets: Edge Varying Graph Neural Networks,
PAMI(44), No. 11, November 2022, pp. 7457-7473.
IEEE DOI
2210
Convolution, Neural networks, Graph neural networks,
Computational complexity, Tools, Laplace equations, Edge varying,
learning on graphs
BibRef
Liu, M.[Meng],
Wang, Z.Y.[Zheng-Yang],
Ji, S.W.[Shui-Wang],
Non-Local Graph Neural Networks,
PAMI(44), No. 12, December 2022, pp. 10270-10276.
IEEE DOI
2212
Sorting, Task analysis, Graph neural networks, Convolution,
Aggregates, Nonhomogeneous media, Calibration,
disassortative graphs
BibRef
Lei, B.Y.[Bai-Ying],
Zhu, Y.[Yun],
Yu, S.Z.[Shuang-Zhi],
Hu, H.[Huoyou],
Xu, Y.[Yanwu],
Yue, G.H.[Guang-Hui],
Wang, T.F.[Tian-Fu],
Zhao, C.[Cheng],
Chen, S.[Shaobin],
Yang, P.[Peng],
Song, X.G.[Xue-Gang],
Xiao, X.H.[Xiao-Hua],
Wang, S.Q.[Shu-Qiang],
Multi-scale enhanced graph convolutional network for mild cognitive
impairment detection,
PR(134), 2023, pp. 109106.
Elsevier DOI
2212
Mild cognitive impairment detection, Multimodal brain connectivity networks,
Multi-scale enhanced graph convolutional network
BibRef
Li, S.[Shuo],
Liu, F.[Fang],
Jiao, L.C.[Li-Cheng],
Chen, P.[Puhua],
Liu, X.[Xu],
Li, L.L.[Ling-Ling],
MFNet: A Novel GNN-Based Multi-Level Feature Network With Superpixel
Priors,
IP(31), 2022, pp. 7306-7321.
IEEE DOI
2212
Feature extraction, Task analysis, Object detection,
Image segmentation, Convolution, Graph neural networks, Shape,
representation learning
BibRef
Bouritsas, G.[Giorgos],
Frasca, F.[Fabrizio],
Zafeiriou, S.P.[Stefanos P.],
Bronstein, M.M.[Michael M.],
Improving Graph Neural Network Expressivity via Subgraph Isomorphism
Counting,
PAMI(45), No. 1, January 2023, pp. 657-668.
IEEE DOI
2212
Orbits, Message passing, Graph neural networks, Color,
Social networking (online), Proteins, Histograms, neural network expressivity
BibRef
Abdelaziz, I.[Ibrahim],
Crouse, M.[Maxwell],
Makni, B.[Bassem],
Austel, V.[Vernon],
Cornelio, C.[Cristina],
Ikbal, S.[Shajith],
Kapanipathi, P.[Pavan],
Makondo, N.[Ndivhuwo],
Srinivas, K.[Kavitha],
Witbrock, M.[Michael],
Fokoue, A.[Achille],
Learning to Guide a Saturation-Based Theorem Prover,
PAMI(45), No. 1, January 2023, pp. 738-751.
IEEE DOI
2212
Standards, Reinforcement learning, Graph neural networks,
Feature extraction, Benchmark testing, Search problems,
graph neural networks
BibRef
Cao, C.Q.[Cong-Qi],
Zhang, X.[Xin],
Zhang, S.Z.[Shi-Zhou],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly
Detection in Videos,
SPLetters(29), 2022, pp. 2497-2501.
IEEE DOI
2212
Videos, Feature extraction, Adaptation models, Training,
Context modeling, Convolution, Anomaly detection, temporal modeling
BibRef
Zhang, S.X.[Shao-Xuan],
Feng, J.[Jian],
Lu, S.[Senxiang],
A novel method for fusing graph convolutional network and feature
based on feedback connection mechanism for nondestructive testing,
PRL(164), 2022, pp. 284-292.
Elsevier DOI
2212
BibRef
Chen, D.W.[Dong-Wen],
Qing, C.M.[Chun-Mei],
Lin, X.[Xu],
Ye, M.T.[Meng-Tao],
Xu, X.M.[Xiang-Min],
Dickinson, P.[Patrick],
Intra- and Inter-Reasoning Graph Convolutional Network for Saliency
Prediction on 360° Images,
CirSysVideo(32), No. 12, December 2022, pp. 8730-8743.
IEEE DOI
2212
Feature extraction, Semantics, Convolution, Predictive models,
Image edge detection, Distortion, Data mining, Virtual reality,
graph convolutional network
BibRef
Wei, F.F.[Fei-Fei],
Ping, M.Z.[Ming-Zhu],
Mei, K.Z.[Kui-Zhi],
Structure-based graph convolutional networks with frequency filter,
PRL(164), 2022, pp. 161-165.
Elsevier DOI
2212
Network representation learning, Node embeddings,
Graph filtering, Graph convolution neural network
BibRef
Xie, Y.C.[Yao-Chen],
Xu, Z.[Zhao],
Zhang, J.T.[Jing-Tun],
Wang, Z.Y.[Zheng-Yang],
Ji, S.W.[Shui-Wang],
Self-Supervised Learning of Graph Neural Networks: A Unified Review,
PAMI(45), No. 2, February 2023, pp. 2412-2429.
IEEE DOI
2301
Task analysis, Predictive models, Data models, Training,
Graph neural networks, Mutual information, Head, Deep learning,
unsupervised learning
BibRef
Zeng, X.[Xuhui],
Wang, S.[Shu],
Zhu, Y.Q.[Yun-Qiang],
Xu, M.F.[Meng-Fei],
Zou, Z.Q.[Zhi-Qiang],
A Knowledge Graph Convolutional Networks Method for Countryside
Ecological Patterns Recommendation by Mining Geographical Features,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link
2301
BibRef
Kazi, A.[Anees],
Cosmo, L.[Luca],
Ahmadi, S.A.[Seyed-Ahmad],
Navab, N.[Nassir],
Bronstein, M.M.[Michael M.],
Differentiable Graph Module (DGM) for Graph Convolutional Networks,
PAMI(45), No. 2, February 2023, pp. 1606-1617.
IEEE DOI
2301
Computational modeling, Task analysis, Convolution,
Training, Topology, Pipelines, disease prediction
BibRef
Wang, X.[Xili],
Liang, Z.Y.[Zheng-Yin],
Hybrid network model based on 3D convolutional neural network and
scalable graph convolutional network for hyperspectral image
classification,
IET-IPR(17), No. 1, 2023, pp. 256-273.
DOI Link
2301
BibRef
Valem, L.P.[Lucas Pascotti],
Guimarăes-Pedronette, D.C.[Daniel Carlos],
Latecki, L.J.[Longin Jan],
Graph Convolutional Networks based on manifold learning for
semi-supervised image classification,
CVIU(227), 2023, pp. 103618.
Elsevier DOI
2301
Manifold learning, Graph Convolutional Networks,
Image classification, Semi-supervised
BibRef
Wu, F.[Fei],
Li, S.[Shuaishuai],
Gao, G.[Guangwei],
Ji, Y.[Yimu],
Jing, X.Y.[Xiao-Yuan],
Wan, Z.G.[Zhi-Guo],
Semi-supervised cross-modal hashing via modality-specific and
cross-modal graph convolutional networks,
PR(136), 2023, pp. 109211.
Elsevier DOI
2301
Cross-modal hashing, semi-supervised learning, graph convolutional networks,
modality-specific features, modality-shared features
BibRef
Sellami, A.[Akrem],
Farah, M.[Mohamed],
Dalla-Mura, M.[Mauro],
SHCNet: A semi-supervised hypergraph convolutional networks based on
relevant feature selection for hyperspectral image classification,
PRL(165), 2023, pp. 98-106.
Elsevier DOI
2301
Unsupervised feature selection, Hypergraph convolutional network,
Dimensionality reduction
BibRef
Huang, C.Q.[Chang-Qin],
Li, M.[Ming],
Cao, F.L.[Fei-Long],
Fujita, H.[Hamido],
Li, Z.[Zhao],
Wu, X.D.[Xin-Dong],
Are Graph Convolutional Networks With Random Weights Feasible?,
PAMI(45), No. 3, March 2023, pp. 2751-2768.
IEEE DOI
2302
Training, Analytical models, Upper bound, Stability analysis,
Neural networks, Convolution, Convolutional neural networks,
approximation upper bound
BibRef
Chen, T.L.[Tian-Long],
Zhou, K.X.[Kai-Xiong],
Duan, K.Y.[Ke-Yu],
Zheng, W.Q.[Wen-Qing],
Wang, P.H.[Pei-Hao],
Hu, X.[Xia],
Wang, Z.Y.[Zhang-Yang],
Bag of Tricks for Training Deeper Graph Neural Networks:
A Comprehensive Benchmark Study,
PAMI(45), No. 3, March 2023, pp. 2769-2781.
IEEE DOI
2302
Training, Benchmark testing, Standards, Peer-to-peer computing,
Graph neural networks, Task analysis, Deep graph neural networks, benchmark
BibRef
Vasudevan, V.[Varun],
Bassenne, M.[Maxime],
Islam, M.T.[Md Tauhidul],
Xing, L.[Lei],
Image classification using graph neural network and multiscale
wavelet superpixels,
PRL(166), 2023, pp. 89-96.
Elsevier DOI
2302
Image classification, GNN, Multiscale superpixel, Wavelet
BibRef
Qian, S.S.[Sheng-Sheng],
Xue, D.[Dizhan],
Fang, Q.[Quan],
Xu, C.S.[Chang-Sheng],
Integrating Multi-Label Contrastive Learning With Dual Adversarial
Graph Neural Networks for Cross-Modal Retrieval,
PAMI(45), No. 4, April 2023, pp. 4794-4811.
IEEE DOI
2303
Semantics, Correlation, Data models, Task analysis,
Graph neural networks, Generative adversarial networks, Training
BibRef
Nikolentzos, G.[Giannis],
Dasoulas, G.[George],
Vazirgiannis, M.[Michalis],
Permute Me Softly: Learning Soft Permutations for Graph
Representations,
PAMI(45), No. 4, April 2023, pp. 5087-5098.
IEEE DOI
2303
Biological system modeling, Computational modeling,
Stochastic processes, Message passing, graph representations
BibRef
Mohamed, H.A.[Hebatallah A.],
Pilutti, D.[Diego],
James, S.[Stuart],
del Bue, A.[Alessio],
Pelillo, M.[Marcello],
Vascon, S.[Sebastiano],
Locality-aware subgraphs for inductive link prediction in knowledge
graphs,
PRL(167), 2023, pp. 90-97.
Elsevier DOI
2303
Knowledge graphs, Inductive link prediction,
Graph neural networks, Local clustering, Personalized PageRank
BibRef
Stankovic´, L.[Ljubiša],
Mandic, D.P.[Danilo P.],
Understanding the Basis of Graph Convolutional Neural Networks via an
Intuitive Matched Filtering Approach,
SPMag(40), No. 2, March 2023, pp. 155-165.
IEEE DOI
2303
Lecture Notes.
Matched filters, Closed box, Convolutional neural networks,
Graph neural networks
BibRef
Bicciato, A.[Alessandro],
Cosmo, L.[Luca],
Minello, G.[Giorgia],
Rossi, L.[Luca],
Torsello, A.[Andrea],
Classifying Me Softly: A Novel Graph Neural Network Based on Features
Soft-Alignment,
SSSPR22(43-53).
Springer DOI
2301
BibRef
Gillioz, A.[Anthony],
Riesen, K.[Kaspar],
Graph Reduction Neural Networks for Structural Pattern Recognition,
SSSPR22(64-73).
Springer DOI
2301
BibRef
Xiao, H.L.[Hao-Liang],
Chen, X.Y.[Xiang-Yang],
Drug ADMET Prediction Method Based on Improved Graph Convolution
Neural Network,
ICRVC22(266-271)
IEEE DOI
2301
Drugs, Toxicology, Machine learning algorithms, Convolution,
Computational modeling, Biological system modeling,
Attention mechanism
BibRef
Seo, S.[Sangwoo],
Jung, S.[Seungjun],
Kim, C.[Changick],
Explanation-based Graph Neural Networks for Graph Classification,
ICPR22(2836-2842)
IEEE DOI
2212
Proteins, Analytical models, Machine learning,
Benchmark testing, Graph neural networks, Data models
BibRef
Wei, Z.[Ziyu],
Xiao, X.[Xi],
Zhang, B.[Bin],
Hu, G.W.[Guang-Wu],
Li, Q.[Qing],
Xia, S.T.[Shu-Tao],
Graph Data Augmentation for Node Classification,
ICPR22(4899-4905)
IEEE DOI
2212
Computational modeling, Benchmark testing, Graph neural networks, Topology
BibRef
Singh, I.P.[Inder Pal],
Ghorbel, E.[Enjie],
Oyedotun, O.[Oyebade],
Aouada, D.[Djamila],
Multi Label Image Classification using Adaptive Graph Convolutional
Networks (ML-AGCN),
ICIP22(1806-1810)
IEEE DOI
2211
Knowledge engineering, Adaptation models, Adaptive systems,
Correlation, Network topology, Convolution, Image processing
BibRef
Kim, J.[Jinwoo],
Oh, S.[Saeyoon],
Cho, S.J.[Sung-Jun],
Hong, S.[Seunghoon],
Equivariant Hypergraph Neural Networks,
ECCV22(XXI:86-103).
Springer DOI
2211
BibRef
Xu, J.Y.[Jia-Yi],
Yang, Q.[Qin],
Li, C.[Chenglin],
Zou, J.[Junni],
Xiong, H.K.[Hong-Kai],
Pan, X.L.[Xin-Long],
Wang, H.[Haipeng],
Rotation-Equivariant Graph Convolutional Networks For Spherical Data
Via Global-Local Attention,
ICIP22(2501-2505)
IEEE DOI
2211
Correlation, Convolution, Data processing, Topology,
Computational efficiency, Kernel, Task analysis, Spherical images,
semantic segmentation
BibRef
Mostafa, A.[Abdelrahman],
Peng, W.[Wei],
Zhao, G.Y.[Guo-Ying],
Hyperbolic Spatial Temporal Graph Convolutional Networks,
ICIP22(3301-3305)
IEEE DOI
2211
Representation learning, Geometry, Image recognition, Distortion,
Hyperbolic geometry, dynamic graphs, human action recognition
BibRef
Guan, Y.H.[Yong-Hang],
Zhang, J.[Jun],
Tian, K.[Kuan],
Yang, S.[Sen],
Dong, P.[Pei],
Xiang, J.X.[Jin-Xi],
Yang, W.[Wei],
Huang, J.Z.[Jun-Zhou],
Zhang, Y.Y.[Yu-Yao],
Han, X.[Xiao],
Node-aligned Graph Convolutional Network for Whole-slide Image
Representation and Classification,
CVPR22(18791-18801)
IEEE DOI
2210
Convolutional codes, Pathology, Correlation, Convolution,
Computational modeling, Image representation, Medical,
Self- semi- meta- unsupervised learning
BibRef
Lin, W.[Wanyu],
Lan, H.[Hao],
Wang, H.[Hao],
Li, B.[Baochun],
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting
Graph Neural Networks,
CVPR22(13719-13728)
IEEE DOI
2210
Visualization, Privacy, Statistical analysis, Semantics,
Training data, Medical services, privacy and ethics in vision, Transparency
BibRef
Schaefer, S.[Simon],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
AEGNN: Asynchronous Event-based Graph Neural Networks,
CVPR22(12361-12371)
IEEE DOI
2210
Code, GNN.
WWW Link. Power demand, Object detection, Market research,
Graph neural networks, Pattern recognition, Object recognition,
Scene analysis and understanding
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.H.[Jia-Hui],
Zhou, L.[Lei],
Bai, X.Y.[Xu-Yang],
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,
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.X.[Nan-Xuan],
Liu, M.H.[Min-Hao],
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],
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Wang, X.C.[Xin-Chao],
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Amalgamating Knowledge from Heterogeneous Graph Neural Networks,
CVPR21(15704-15713)
IEEE DOI
2111
Knowledge engineering, Convolutional codes,
Annotations, Semantics, 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
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Hoang, N.T.,
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Revisiting Graph Neural Networks: Graph Filtering Perspective,
ICPR21(8376-8383)
IEEE DOI
2105
Convolutional codes, Analytical models, Filtering, Convolution,
Graph neural networks
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Lyu, Y.C.[Ye-Cheng],
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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
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Dominguez, M.[Miguel],
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Directional Graph Networks with Hard Weight Assignments,
ICPR21(7439-7446)
IEEE DOI
2105
Convolution, Computational modeling,
Neural networks, Robot sensing systems, Computational efficiency, Sensors
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Zhang, Y.H.[Yu-Hang],
Ren, H.S.[Hong-Shuai],
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AOAM: Automatic Optimization of Adjacency Matrix for Graph
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ICPR21(5130-5136)
IEEE DOI
2105
Training, Correlation, Convolution, Heuristic algorithms, Focusing,
Reinforcement learning, Search problems, Node Information Entropy
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Sahbi, H.[Hichem],
Kernel-based Graph Convolutional Networks,
ICPR21(4887-4894)
IEEE DOI
2105
Training, Convolution, Image color analysis, Training data,
Hilbert space, Kernel
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Li, Z.X.[Zhi-Xin],
Sun, Y.[Yaru],
Tang, S.[Suqin],
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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
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Deng, J.[Jiehui],
Wan, S.[Sheng],
Wang, X.[Xiang],
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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
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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
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Wang, C.[Chen],
Deng, C.Y.[Cheng-Yuan],
On the Global Self-attention Mechanism for Graph Convolutional
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ICPR21(8531-8538)
IEEE DOI
2105
Benchmark testing, Pattern recognition,
Convolutional neural networks, Task analysis
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Yang, L.[Lei],
Huang, Q.Q.[Qing-Qiu],
Huang, H.Y.[Huai-Yi],
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exploiting unlabeled data.
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Kim, C.,
Self-Training Of Graph Neural Networks Using Similarity Reference For
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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.
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Adaloglou, N.[Nikolas],
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Multi-view Adaptive Graph Convolutions for Graph Classification,
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Zhang, X.K.[Xi-Kun],
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2011
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Ddgcn: A Dynamic Directed Graph Convolutional Network for Action
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Springer DOI
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Yu, C.Q.[Chang-Qian],
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Representative Graph Neural Network,
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2011
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Wang, C.[Chu],
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Affinity Graph Supervision for Visual Recognition,
CVPR20(8244-8252)
IEEE DOI
2008
Visualization, Training, Task analysis, Manganese, Proposals,
Convolutional neural networks
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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
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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
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Lin, J.,
Yuan, Y.,
Shao, T.,
Zhou, K.,
Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images
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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
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CVPR20(9047-9056)
IEEE DOI
2008
Feature extraction, Task analysis, Adaptive systems, Decoding,
Saliency detection, Visualization, Convolution
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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
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ICCV19(6518-6527)
IEEE DOI
2004
data visualisation, decoding, encoding, graph theory,
image representation, learning (artificial intelligence),
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Mosella-Montoro, A.,
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Residual Attention Graph Convolutional Network for Geometric 3D Scene
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GMDL19(4123-4132)
IEEE DOI
2004
computational geometry, convolutional neural nets,
feature extraction, image classification, image colour analysis,
agc
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Sun, H.L.[Hao-Liang],
Zhen, X.T.[Xian-Tong],
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Learning the Set Graphs:
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ICIP19(4554-4558)
IEEE DOI
1910
Set graph learning, Graph convolutional network, l1,2-Norm,
Image-set classification
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Chen, Z.M.[Zhao-Min],
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Multi-Label Image Recognition With Graph Convolutional Networks,
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IEEE DOI
2002
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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
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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
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Edwards, M.[Michael],
Xie, X.H.[Xiang-Hua],
Graph Convolutional Neural Network,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks, Design, Implementation Issues .