Jiang, B.[Bo],
Zhao, K.K.[Kang-Kang],
Tang, J.[Jin],
RGTransformer: Region-Graph Transformer for Image Representation and
Few-Shot Classification,
SPLetters(29), 2022, pp. 792-796.
IEEE DOI
2204
Measurement, Transformers, Image representation,
Feature extraction, Visualization, transformer
BibRef
Yeom, J.[Jeyoon],
Kim, T.[Taero],
Chang, R.[Rakwoo],
Song, K.[Kyungwoo],
Structural and positional ensembled encoding for Graph Transformer,
PRL(183), 2024, pp. 104-110.
Elsevier DOI
2406
Graph neural network, Graph Transformer, Positional encoding,
Graph clustering, Attention
BibRef
Wang, X.X.[Xi-Xi],
Jiang, B.[Bo],
Wang, X.[Xiao],
Tang, J.H.[Jin-Hui],
Luo, B.[Bin],
Rethinking Batch Sample Relationships for Data Representation:
A Batch-Graph Transformer Based Approach,
MultMed(26), 2024, pp. 1578-1588.
IEEE DOI
2402
Transformers, Task analysis, Visualization,
Representation learning, Feature extraction, metric learning
BibRef
Chen, C.Q.[Chao-Qi],
Wu, Y.S.[Yu-Shuang],
Dai, Q.Y.[Qi-Yuan],
Zhou, H.Y.[Hong-Yu],
Xu, M.[Mutian],
Yang, S.[Sibei],
Han, X.G.[Xiao-Guang],
Yu, Y.Z.[Yi-Zhou],
A Survey on Graph Neural Networks and Graph Transformers in Computer
Vision: A Task-Oriented Perspective,
PAMI(46), No. 12, December 2024, pp. 10297-10318.
IEEE DOI
2411
Task analysis, Transformers, Point cloud compression,
Visualization, Videos, graph transformers, graph neural networks,
vision and language
BibRef
Li, Y.[Ying],
Li, L.L.[Lin-Lin],
Liu, X.Y.[Xiang-Yu],
Liu, Y.J.[Yi-Jun],
Li, Q.Q.[Qian-Qian],
Influence maximization for heterogeneous networks based on
self-supervised clustered heterogeneous graph transformer,
PR(154), 2024, pp. 110595.
Elsevier DOI
2406
Influence maximization, Heterogeneous network,
Clustering information, Heterogeneous graph transformer
BibRef
Ding, Y.[Yun],
Xu, C.C.[Cheng-Chao],
Wei, P.[Pijing],
Cao, R.[Ruifen],
Hang, R.[Renlong],
Zheng, C.[Chunhou],
Graph Transformer With Structural Embedding and Training for
Hyperspectral Image,
CirSysVideo(36), No. 5, May 2026, pp. 5954-5967.
IEEE DOI Code:
WWW Link.
2605
Transformers, Training, Encoding, Computer architecture,
Symmetric matrices, Semantics, Vectors, Sparse matrices, structure training
BibRef
Chen, H.J.[Hong-Jiang],
Jiao, P.F.[Peng-Fei],
Du, M.[Ming],
Guo, X.[Xuan],
Zhao, Z.D.[Zhi-Dong],
Jin, D.[Di],
Liu, X.[Xiao],
TGFormer: Towards temporal graph transformer with auto-correlation
mechanism,
PR(170), 2026, pp. 112053.
Elsevier DOI
2509
Temporal graph, Graph transformer, Graph neural network, Representation learning
BibRef
Nakhli, R.[Ramin],
Moghadam, P.A.[Puria Azadi],
Mi, H.Y.[Hao-Yang],
Farahani, H.[Hossein],
Baras, A.[Alexander],
Gilks, B.[Blake],
Bashashati, A.[Ali],
Sparse Multi-Modal Graph Transformer with Shared-Context Processing
for Representation Learning of Giga-pixel Images,
CVPR23(11547-11557)
IEEE DOI
2309
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
U-Net, Convolutional Neural Networks .