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
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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
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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
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
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
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
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
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
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
Yuan, M.R.[Meng-Ru],
Zhang, H.X.[Hua-Xiang],
Liu, D.M.[Dong-Mei],
Wang, L.[Lin],
Liu, L.[Li],
Semantic-embedding Guided Graph Network for cross-modal retrieval,
JVCIR(93), 2023, pp. 103807.
Elsevier DOI
2305
Cross-modal retrieval, Graph convolution network,
Adversarial network, Graph aggregation network
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
Tinega, H.C.[Haron C.],
Chen, E.[Enqing],
Nyasaka, D.O.[Divinah O.],
Improving Feature Learning in Remote Sensing Images Using an
Integrated Deep Multi-Scale 3D/2D Convolutional Network,
RS(15), No. 13, 2023, pp. 3270.
DOI Link
2307
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
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
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
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
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
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
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.B.[Shao-Bin],
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
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
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],
Guimarães-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
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
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
Kong, Y.Y.[You-Yong],
Li, J.X.[Jia-Xing],
Zhang, K.[Ke],
Wu, J.S.[Jia-Song],
Multi-scale self-attention mixup for graph classification,
PRL(168), 2023, pp. 100-106.
Elsevier DOI
2304
Graph convolutional network, Self-Attention, Mixup, Graph classification
BibRef
Zhang, G.[Guolin],
Hu, Z.[Zehui],
Wen, G.Q.[Guo-Qiu],
Ma, J.[Junbo],
Zhu, X.F.[Xiao-Feng],
Dynamic graph convolutional networks by semi-supervised contrastive
learning,
PR(139), 2023, pp. 109486.
Elsevier DOI
2304
Topology, Dynamic feature graph, Semi-supervised contrastive learning
BibRef
Liu, W.K.[Wen-Kai],
Liu, B.[Bing],
He, P.P.[Pei-Pei],
Hu, Q.F.[Qing-Feng],
Gao, K.L.[Kui-Liang],
Li, H.[Hui],
Masked Graph Convolutional Network for Small Sample Classification of
Hyperspectral Images,
RS(15), No. 7, 2023, pp. 1869.
DOI Link
2304
BibRef
Chen, Z.M.[Zhao-Min],
Wei, X.S.[Xiu-Shen],
Wang, P.[Peng],
Guo, Y.[Yanwen],
Learning Graph Convolutional Networks for Multi-Label Recognition and
Applications,
PAMI(45), No. 6, June 2023, pp. 6969-6983.
IEEE DOI
2305
BibRef
Earlier:
Multi-Label Image Recognition With Graph Convolutional Networks,
CVPR19(5172-5181).
IEEE DOI
2002
Image recognition, Correlation, Face recognition, Task analysis,
Semantics, Topology, Computational modeling,
label dependency
BibRef
Wei, X.[Xin],
Yu, R.X.[Rui-Xuan],
Sun, J.[Jian],
Learning View-Based Graph Convolutional Network for Multi-View 3D
Shape Analysis,
PAMI(45), No. 6, June 2023, pp. 7525-7541.
IEEE DOI
2305
BibRef
Earlier:
View-GCN: View-Based Graph Convolutional Network for 3D Shape
Analysis,
CVPR20(1847-1856)
IEEE DOI
2008
Shape, Convolution, Feature extraction,
Image recognition, Aggregates, Solid modeling,
Multi-view 3D shape recognition, view-graph, rotation robustness.
Aggregates, Image recognition
BibRef
Li, G.H.[Guo-Hao],
Müller, M.[Matthias],
Qian, G.[Guocheng],
Delgadillo, I.C.[Itzel C.],
Abualshour, A.[Abdulellah],
Thabet, A.[Ali],
Ghanem, B.[Bernard],
DeepGCNs: Making GCNs Go as Deep as CNNs,
PAMI(45), No. 6, June 2023, pp. 6923-6939.
IEEE DOI
2305
Training, Task analysis, Semantics, Convolutional codes,
Image segmentation, Biological system modeling,
deep learning
BibRef
Lyu, Y.C.[Ye-Cheng],
Huang, X.M.[Xin-Ming],
Zhang, Z.M.[Zi-Ming],
Revisiting 2D Convolutional Neural Networks for Graph-Based
Applications,
PAMI(45), No. 6, June 2023, pp. 6909-6922.
IEEE DOI
2305
Layout, Feature extraction, Topology, Training, Neural networks,
Convolution, Graph neural network, convolutional neural network,
3D point cloud segmentation
BibRef
Xu, D.W.[Dong-Wei],
Shang, X.[Xuetian],
Peng, H.[Hang],
Li, H.[Haijian],
MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution
Network Based Trajectory Prediction for Heterogeneous Traffic-Agents,
ITS(24), No. 6, June 2023, pp. 6217-6226.
IEEE DOI
2306
Trajectory, Predictive models, Convolution, Correlation,
Adaptive systems, Adaptation models, Hidden Markov models,
graph neural network
BibRef
Zhang, H.Y.[Hong-Yuan],
Shi, J.K.[Jian-Kun],
Zhang, R.[Rui],
Li, X.L.[Xue-Long],
Non-Graph Data Clustering via O(n) Bipartite Graph Convolution,
PAMI(45), No. 7, July 2023, pp. 8729-8742.
IEEE DOI
2306
Convolution, Clustering methods, Bipartite graph,
Feature extraction, Data mining, Computational modeling, Training,
siamese network
BibRef
Wang, M.[Min],
Zhou, W.G.[Wen-Gang],
Tian, Q.[Qi],
Li, H.Q.[Hou-Qiang],
Deep Graph Convolutional Quantization Networks for Image Retrieval,
MultMed(25), 2023, pp. 2164-2175.
IEEE DOI
2306
Combine Deep NN and Graph CNN
Quantization (signal), Databases, Manifolds, Training, Semantics,
Convolutional neural networks, Binary codes, Deep quantization, image retrieval
BibRef
Wei, M.Q.[Ming-Qiang],
Wei, Z.Y.[Ze-Yong],
Zhou, H.R.[Hao-Ran],
Hu, F.[Fei],
Si, H.J.[Hua-Jian],
Chen, Z.L.[Zhi-Lei],
Zhu, Z.[Zhe],
Qiu, J.[Jingbo],
Yan, X.F.[Xue-Feng],
Guo, Y.[Yanwen],
Wang, J.[Jun],
Qin, J.[Jing],
AGConv: Adaptive Graph Convolution on 3D Point Clouds,
PAMI(45), No. 8, August 2023, pp. 9374-9392.
IEEE DOI
2307
Point cloud compression, Convolution, Feature extraction, Kernel,
Shape, Deep learning, Adaptive graph convolution,
geometric deep learning
BibRef
Liang, Q.[Qi],
Li, Q.[Qiang],
Nie, W.Z.[Wei-Zhi],
Liu, A.A.[An-An],
Unsupervised Cross-Media Graph Convolutional Network for 2D
Image-Based 3D Model Retrieval,
MultMed(25), 2023, pp. 3443-3455.
IEEE DOI
2309
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
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
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.P.[Hai-Peng],
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
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
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
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
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, Graph neural networks
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
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
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
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
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,
Convolutional neural networks
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
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
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks, Design, Implementation Issues .