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Graph Neural Networks (GNNs) have received increasing attention in many fields.
“Auto-encoding variational bayes,”
Diederik P Kingma and Max Welling, · 2013
Earlier work this paper cites.
“Semi-supervised classification with graph convolutional networks,”
Thomas N Kipf and Max Welling, · 2016
Earlier work this paper cites.
“Variational graph auto-encoders,”
Thomas N Kipf and Max Welling, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“V-net: Fully convolutional neural networks for volumetric medical image segmentation,”
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi, · 2016
Earlier work this paper cites.
“Semantic segmentation of earth observation data using multimodal and multi-scale deep networks,”
Nicolas Audebert, Bertrand Le Saux, and Sébastien Lefèvre, · 2016
Earlier work this paper cites.
“Classification with an edge: Improving semantic image segmentation with boundary detection,”
Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, and Uwe Stilla, · 2016
Cited alongside, same era.
“Geometric deep learning: going beyond euclidean data,”
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst, · 2017
Cited alongside, same era.
“Gated convolutional neural network for semantic segmentation in high-resolution images,”
Hongzhen Wang, Ying Wang, Qian Zhang, Shiming Xiang, and Chunhong Pan, · 2017
Cited alongside, same era.
“Symbolic graph reasoning meets convolutions,”
Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, and Eric P Xing, · 2018
Cited alongside, same era.
“2D Semantic Labeling Contest,” online, 2018
International Society for Photogrammetry and Remote Sensing (ISPRS), · 2018
Cited alongside, same era.
“A comparison of deep learning architectures for semantic mapping of very high resolution images,”
Q. Liu, A. Salberg, and R. Jenssen, · 2018
Later among the works it cites.
“Image classification with hierarchical multigraph networks,”
Boris Knyazev, Xiao Lin, Mohamed R Amer, and Graham W Taylor, · 2019
Later among the works it cites.
“Rethinking knowledge graph propagation for zero-shot learning,”
Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, and Eric P Xing, · 2019
Later among the works it cites.
“Dynamic graph cnn for learning on point clouds,”
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon, · 2019
Later among the works it cites.
“Dense dilated convolutions merging network for semantic mapping of remote sensing images,”
Qinghui liu, Michael Kampffmeyer, Robert Jenssen, and Arnt-Borre Salberg, · 2019
Later among the works it cites.
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