14.1.9.2 Deep Few Shot Learning

Liu, B.[Bing], Yu, X.C.[Xu-Chu], Yu, A.Z.[An-Zhu], Zhang, P.Q.[Peng-Qiang], Wan, G.[Gang], Wang, R.R.[Rui-Rui],
Deep Few-Shot Learning for Hyperspectral Image Classification,
GeoRS(57), No. 4, April 2019, pp. 2290-2304.
IEEE DOI 1904
convolutional neural nets, geophysical image processing, hyperspectral imaging, image classification, residual learning BibRef

Liu, B.[Bing], Yu, A.Z.[An-Zhu], Yu, X.C.[Xu-Chu], Wang, R.R.[Rui-Rui], Gao, K.L.[Kui-Liang], Guo, W.Y.[Wen-Yue],
Deep Multiview Learning for Hyperspectral Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7758-7772.
IEEE DOI 2109
Training, Support vector machines, Radio frequency, Deep learning, Task analysis, Unsupervised learning, Residual neural networks, small samples BibRef

Gao, K.L.[Kui-Liang], Liu, B.[Bing], Yu, X.C.[Xu-Chu], Qin, J.C.[Jin-Chun], Zhang, P.Q.[Peng-Qiang], Tan, X.[Xiong],
Deep Relation Network for Hyperspectral Image Few-Shot Classification,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Qin, Y., Zhang, W., Wang, Z., Zhao, C., Shi, J.,
Layer-Wise Adaptive Updating for Few-Shot Image Classification,
SPLetters(27), 2020, pp. 2044-2048.
IEEE DOI 2012
Deep learning, few-shot image classification, layer-wise adaptive updating, meta-learning BibRef

Zhang, H.J.[Hong-Jing], Zhan, T.Y.[Tian-Yang], Davidson, I.[Ian],
A Self-Supervised Deep Learning Framework for Unsupervised Few-Shot Learning and Clustering,
PRL(148), 2021, pp. 75-81.
Elsevier DOI 2107
Deep Learning, Unsupervised Representation Learning, Unsupervised Few-shot Learning, Clustering BibRef

Zhang, L.[Lamei], Zhang, S.[Siyu], Zou, B.[Bin], Dong, H.W.[Hong-Wei],
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Training, Task analysis, Optical imaging, Annotations, Optical sensors, Neural networks, Training data, unsupervised representation learning BibRef

Chen, C.[Cen], Li, K.L.[Ken-Li], Wei, W.[Wei], Zhou, J.T.Y.[Joey Tian-Yi], Zeng, Z.[Zeng],
Hierarchical Graph Neural Networks for Few-Shot Learning,
CirSysVideo(32), No. 1, January 2022, pp. 240-252.
IEEE DOI 2201
Cognition, Feature extraction, Graph neural networks, Training, Task analysis, Deep learning, Predictive models, Few-shot learning, hierarchical structure BibRef

Cheng, H.[Hao], Zhou, J.T.Y.[Joey Tian-Yi], Tay, W.P.[Wee Peng], Wen, B.[Bihan],
Graph Neural Networks With Triple Attention for Few-Shot Learning,
MultMed(25), 2023, pp. 8225-8239.
IEEE DOI 2312
BibRef

Li, Y.F.[Yang-Fan], Chen, C.[Cen], Yan, W.Q.[Wei-Quan], Cheng, Z.Y.[Zhong-Yao], Tan, H.L.[Hui Li], Zhang, W.J.[Wen-Jie],
Cascade Graph Neural Networks for Few-Shot Learning on Point Clouds,
ITS(24), No. 8, August 2023, pp. 8788-8798.
IEEE DOI 2308
Point cloud compression, Training, Task analysis, Feature extraction, Graph neural networks, Deep learning, point clouds BibRef

Li, N.[Na], Zhou, D.Y.[De-Yun], Shi, J.[Jiao], Zheng, X.L.[Xiao-Long], Wu, T.[Tao], Yang, Z.[Zhen],
Graph-Based Deep Multitask Few-Shot Learning for Hyperspectral Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Luo, J.J.[Jia-Ji], Si, W.J.[Wei-Jian], Deng, Z.[Zhian],
Few-Shot Learning for Radar Signal Recognition Based on Tensor Imprint and Re-Parameterization Multi-Channel Multi-Branch Model,
SPLetters(29), 2022, pp. 1327-1331.
IEEE DOI 2206
Convolution, Radar, Tensors, Training, Time-frequency analysis, Radar imaging, Kernel, Deep learning, few-shot learning, structural re-parameterization BibRef

Chi, Z.Q.[Zi-Qiu], Wang, Z.[Zhe], Yang, M.P.[Meng-Ping], Li, D.D.[Dong-Dong], Du, W.L.[Wen-Li],
Learning to Capture the Query Distribution for Few-Shot Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4163-4173.
IEEE DOI 2207
Prototypes, Measurement, Task analysis, Cognition, Training, Adaptation models, Standards, Few-shot learning, deep learning BibRef

Zhu, P.F.[Peng-Fei], Zhu, Z.L.[Zhi-Lin], Wang, Y.[Yu], Zhang, J.L.[Jing-Lin], Zhao, S.[Shuai],
Multi-granularity episodic contrastive learning for few-shot learning,
PR(131), 2022, pp. 108820.
Elsevier DOI 2208
Multi-granularity computing, Episodic contrastive learning, Few-shot learning, Deep learning BibRef

Dai, Z.Y.[Zhi-Yong], Yi, J.J.[Jian-Jun], Yan, L.[Lei], Xu, Q.W.[Qing-Wen], Hu, L.[Liang], Zhang, Q.[Qi], Li, J.H.[Jia-Hui], Wang, G.Q.[Guo-Qiang],
PFEMed: Few-shot medical image classification using prior guided feature enhancement,
PR(134), 2023, pp. 109108.
Elsevier DOI 2212
Deep learning, Domain adaption, Few-shot learning, Medical image classification, Variational autoencoder BibRef

Chen, Y.D.[Ya-Dang], Yan, H.[Hui], Yang, Z.X.[Zhi-Xin], Wu, E.[Enhua],
Meta-transfer-adjustment learning for few-shot learning,
JVCIR(89), 2022, pp. 103678.
Elsevier DOI 2212
Few-shot learning, Deep neural networks, Feature adjustment, Task adjustment BibRef

Chen, Y.Q.[Yan-Qiao], Li, Y.Y.[Yang-Yang], Mao, H.[Heting], Chai, X.H.[Xing-Hua], Jiao, L.C.[Li-Cheng],
A Novel Deep Nearest Neighbor Neural Network for Few-Shot Remote Sensing Image Scene Classification,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Chen, Y.Q.[Yan-Qiao], Li, Y.Y.[Yang-Yang], Mao, H.[Heting], Liu, G.Y.[Guang-Yuan], Chai, X.H.[Xing-Hua], Jiao, L.C.[Li-Cheng],
A Novel Discriminative Enhancement Method for Few-Shot Remote Sensing Image Scene Classification,
RS(15), No. 18, 2023, pp. 4588.
DOI Link 2310
BibRef

Zhang, C.[Chi], Cai, Y.J.[Yu-Jun], Lin, G.S.[Guo-Sheng], Shen, C.H.[Chun-Hua],
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning,
PAMI(45), No. 5, May 2023, pp. 5632-5648.
IEEE DOI 2304
BibRef
Earlier:
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers,
CVPR20(12200-12210)
IEEE DOI 2008
Task analysis, Earth, Training, Optimal matching, Optimization, Neural networks, Costs, Few-shot classification, meta learning, metric learning. Optimal matching, Earth, Training, Measurement, Image representation networks BibRef

Zhong, X.[Xian], Gu, C.[Cheng], Ye, M.[Mang], Huang, W.X.[Wen-Xin], Lin, C.W.[Chia-Wen],
Graph Complemented Latent Representation for Few-Shot Image Classification,
MultMed(25), 2023, pp. 1979-1990.
IEEE DOI 2306
Task analysis, Training, Feature extraction, Image classification, Probabilistic logic, Image reconstruction, Deep learning, variational inference BibRef

Zhong, X.[Xian], Gu, C.[Cheng], Huang, W.X.[Wen-Xin], Li, L.[Lin], Chen, S.Q.[Shu-Qin], Lin, C.W.[Chia-Wen],
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach,
ICPR21(2677-2684)
IEEE DOI 2105
Training, Feature extraction, Probabilistic logic, Task analysis, Standards, Variational inference BibRef

Fu, W.[Wen], Zhou, L.[Li], Chen, J.[Jie],
Query-Specific Embedding Co-Adaptation Improve Few-Shot Image Classification,
SPLetters(30), 2023, pp. 783-787.
IEEE DOI 2307
Task analysis, Prototypes, Feature extraction, Training, Adaptation models, Standards, Image classification, Deep learning, embedding adaptation BibRef

Zhang, G.M.[Guang-Ming], Zhao, Y.[Yaliang], Wang, J.[Jinke],
Few-shot node classification on attributed networks based on deep metric learning for Cyber-Physical-Social Services,
PRL(173), 2023, pp. 87-92.
Elsevier DOI 2310
Node classification, Attributed networks, Few-shot learning, Node importance, CPSS BibRef

San-Emeterio, M.G.[Miguel G.],
A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning,
HBAxSCES22(14-25).
Springer DOI 2208
BibRef

Afrasiyabi, A.[Arman], Larochelle, H.[Hugo], Lalonde, J.F.[Jean-François], Gagné, C.[Christian],
Matching Feature Sets for Few-Shot Image Classification,
CVPR22(9004-9014)
IEEE DOI 2210
BibRef
Earlier: A1, A3, A4, Only:
Mixture-based Feature Space Learning for Few-shot Image Classification,
ICCV21(9021-9031)
IEEE DOI 2203
Measurement, Training, Feature extraction, Standards, Transfer/low-shot/long-tail learning, Deep learning architectures and techniques. Clustering algorithms, Mixture models, Classification algorithms, Recognition and classification BibRef

Yin, C.X.[Cheng-Xiang], Wu, K.[Kun], Che, Z.P.[Zheng-Ping], Jiang, B.[Bo], Xu, Z.Y.[Zhi-Yuan], Tang, J.[Jian],
Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning,
ICCV21(2157-2166)
IEEE DOI 2203
Integrated circuits, Deep learning, Visualization, Semantics, Training data, Genomics, Visual reasoning and logical representation BibRef

Osahor, U.[Uche], Nasrabadi, N.M.[Nasser M.],
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning,
WACV22(2040-2049)
IEEE DOI 2202
Computational modeling, Pipelines, Diversity reception, Data models, Stability analysis, Semi- and Un- supervised Learning Deep Learning -> Efficient Training and Inference Methods for Networks BibRef

Garg, A.[Ashima], Bagga, S.[Shaurya], Singh, Y.[Yashvardhan], Anand, S.[Saket],
HierMatch: Leveraging Label Hierarchies for Improving Semi-Supervised Learning,
WACV22(2061-2070)
IEEE DOI 2202
Costs, Semisupervised learning, Benchmark testing, Labeling, Deep Learning Transfer, Few-shot, Semi- and Un- supervised Learning BibRef

Yang, F.Y.[Feng-Yuan], Wang, R.P.[Rui-Ping], Chen, X.L.[Xi-Lin],
Semantic Guided Latent Parts Embedding for Few-Shot Learning,
WACV23(5436-5446)
IEEE DOI 2302
BibRef
Earlier:
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning,
WACV22(1586-1596)
IEEE DOI 2202
Training, Deep learning, Visualization, Semantics, Training data, Prototypes, Generators, and algorithms (including transfer). Knowledge engineering, Correlation, Prototypes, Transfer, Few-shot, Semi- and Un- supervised Learning BibRef

Ye, C.G.[Chuang-Guan], Zhu, H.Y.[Hong-Yuan], Liao, Y.B.[Yong-Bin], Zhang, Y.G.[Yang-Gang], Chen, T.[Tao], Fan, J.Y.[Jia-Yuan],
What Makes for Effective Few-shot Point Cloud Classification?,
WACV22(267-276)
IEEE DOI 2202
Point cloud compression, Deep learning, Solid modeling, Adaptation models, Systematics, 3D Computer Vision BibRef

Pahde, F.[Frederik], Puscas, M.[Mihai], Klein, T.[Tassilo], Nabi, M.[Moin],
Multimodal Prototypical Networks for Few-shot Learning,
WACV21(2643-2652)
IEEE DOI 2106
Training, Learning systems, Deep learning, Visualization, Prototypes BibRef

Luo, Q.X.[Qin-Xuan], Wang, L.F.[Ling-Feng], Lv, J.[Jingguo], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Few-Shot Learning via Feature Hallucination with Variational Inference,
WACV21(3962-3971)
IEEE DOI 2106
Training, Deep learning, Computational modeling, Gaussian distribution, Data models BibRef

Hamidouche, M., Lassance, C.[Carlos], Hu, Y., Drumetz, L., Pasdeloup, B., Gripon, V.[Vincent],
Improving Classification Accuracy With Graph Filtering,
ICIP21(334-338)
IEEE DOI 2201
Training, Filtering, Training data, Machine learning, Benchmark testing, Feature extraction, graph filtering, deep learning BibRef

Minami, S.[Soma], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Knowledge Transfer Graph for Deep Collaborative Learning,
ACCV20(IV:203-217).
Springer DOI 2103
BibRef

Kim, J., Kim, M., Kim, J.U., Lee, H.J., Lee, S., Hong, J., Ro, Y.M.,
Learning Style Correlation for Elaborate Few-Shot Classification,
ICIP20(1791-1795)
IEEE DOI 2011
Feature extraction, Measurement, Correlation, Data mining, Task analysis, Machine learning, Visualization, Deep learning, Few-shot classification BibRef

Sbai, O.[Othman], Couprie, C.[Camille], Aubry, M.[Mathieu],
Unsupervised Image Decomposition in Vector Layers,
ICIP20(1576-1580)
IEEE DOI 2011
Deep Image generation, unsupervised learning BibRef

Sbai, O.[Othman], Couprie, C.[Camille], Aubry, M.[Mathieu],
Impact of Base Dataset Design on Few-shot Image Classification,
ECCV20(XVI: 597-613).
Springer DOI 2010
BibRef

Seo, S.[Seonguk], Seo, P.H.[Paul Hongsuck], Han, B.H.[Bo-Hyung],
Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences,
CVPR19(9022-9030).
IEEE DOI 2002
BibRef

Lin, C., Wang, Y.F., Lei, C., Chen, K.,
Semantics-Guided Data Hallucination for Few-Shot Visual Classification,
ICIP19(3302-3306)
IEEE DOI 1910
Few-shot learning, deep learning, image classification, data hallucination BibRef

Chu, W., Wang, Y.F.,
Learning Semantics-Guided Visual Attention for Few-Shot Image Classification,
ICIP18(2979-2983)
IEEE DOI 1809
Task analysis, Training, Feature extraction, Visualization, Semantics, Generators, Silicon, Few-shot learning, image classification BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
One Shot Learning .