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1901
Graph convolutional neural networks, Spatial construction,
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ICPR18(1024-1029)
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1812
Convolution, Feature extraction, Convolutional neural networks,
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1903
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2006
Teacher-student models, Self-ensemble learning,
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2008
Multi-video summarization, Graph convolutional network, Class imbalance problem
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2008
Network representation learning, Heterogeneous information, Autoencoder
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2009
Deep learning, Learning systems, Convolutional neural networks,
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Wang, H.,
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2009
Tagging, Acoustics, Spectrogram, Training, Convolution,
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IEEE DOI
2010
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ICCV17(5880-5888)
IEEE DOI
1802
Convolution, Neural networks, Message passing, Laplace equations,
Aggregates, Pattern recognition, Function approximation,
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feature extraction, image classification, iterative methods,
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Liu, Y.B.[Yan-Bei],
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1806
Graph representation learning, Community structure, Graph convolutional networks
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1806
Multi-label image recognition, Graph convolutional networks,
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2011
Graph learning, Hypergraph learning, Graph neural networks,
Semi-supervised learning
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Gama, F.,
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Graphs, Convolutions, and Neural Networks: From Graph Filters to
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IEEE DOI
2011
Convolution, Finite impulse response filters,
Autoregressive processes, Network topology, Information filters,
Graphical models
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DOI Link
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Li, Y.S.[Yan-Sheng],
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Multi-Label Remote Sensing Image Scene Classification by Combining a
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DOI Link
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Dong, W.[Wei],
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MobileGCN applied to low-dimensional node feature learning,
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Elsevier DOI
2102
Graph convolutional networks, Affinity-aware encoding, Updater,
Depth-wise separable graph convolution, Low-Dimensional node features
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Sun, B.,
Zhang, H.,
Wu, Z.,
Zhang, Y.,
Li, T.,
Adaptive Spatiotemporal Graph Convolutional Networks for Motor
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SPLetters(28), 2021, pp. 219-223.
IEEE DOI
2102
Convolution, Electroencephalography, Spatiotemporal phenomena,
Feature extraction, Electrodes, Task analysis, Adaptive systems,
spatiotemporal structure
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Pu, S.L.[Sheng-Liang],
Wu, Y.F.[Yuan-Feng],
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RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Jiang, J.J.[Jun-Jie],
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Learning to transfer focus of graph neural network for scene graph
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PR(112), 2021, pp. 107707.
Elsevier DOI
2102
Semantic relationship, Graphical focus, Scene graph,
Class imbalance, Image understanding
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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
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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
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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
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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
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.[Zhiyuan],
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
Zhang, L.M.[Ling-Ming],
Zhao, Y.[Yue],
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],
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream
Graph Convolutional Network for 3D Dental Model Segmentation,
CVPR21(6695-6704)
IEEE DOI
2111
Solid modeling, Shape, Surgery, Teeth,
Predictive models, Feature extraction
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
Patil, A.G.[Akshay Gadi],
Li, M.[Manyi],
Fisher, M.[Matthew],
Savva, M.[Manolis],
Zhang, H.[Hao],
LayoutGMN: Neural Graph Matching for Structural Layout Similarity,
CVPR21(11043-11052)
IEEE DOI
2111
Measurement, Deep learning, Convolution,
Computational modeling, Layout, Graph neural networks
BibRef
Qian, G.C.[Guo-Cheng],
Abualshour, A.[Abdulellah],
Li, G.[Guohao],
Thabet, A.[Ali],
Ghanem, B.[Bernard],
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks,
CVPR21(11678-11687)
IEEE DOI
2111
Convolutional codes, Pipelines, Performance gain,
Feature extraction, Pattern recognition
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
Carbonell, M.[Manuel],
Riba, P.[Pau],
Villegas, M.[Mauricio],
Fornés, A.[Alicia],
Lladós, J.[Josep],
Named Entity Recognition and Relation Extraction with Graph Neural
Networks in Semi Structured Documents,
ICPR21(9622-9627)
IEEE DOI
2105
Information retrieval, Graph neural networks, Data mining,
Task analysis, Tuning, Testing, Strain,
Graph Neural Networks
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
BibRef
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
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
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
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
Zhang, L.[Ling],
Zhu, Z.G.[Zhi-Gang],
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 .