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
Xu, B.R.[Bing-Rong],
Zeng, Z.G.[Zhi-Gang],
Lian, C.[Cheng],
Ding, Z.M.[Zheng-Ming],
Few-Shot Domain Adaptation via Mixup Optimal Transport,
IP(31), 2022, pp. 2518-2528.
IEEE DOI
2204
Adaptation models, Training, Numerical models, Couplings,
Feature extraction, Deep learning, Automation, Few-shot learning,
data augmentation
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,
Pattern recognition, 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,
Market research, 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 .