14.1.8 Few Shot Learning

Fei-Fei, L.[Li], Fergus, R.[Rob], Perona, P.[Pietro],
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories,
CVIU(106), No. 1, April 2007, pp. 59-70.
Elsevier DOI 0704
BibRef
Earlier: GenModel04(178).
IEEE DOI 0406
Object recognition; Categorization; Generative model; Incremental learning; Bayesian model BibRef

Fei-Fei, L.[Li], Perona, P.[Pietro],
A Bayesian Hierarchical Model for Learning Natural Scene Categories,
CVPR05(II: 524-531).
IEEE DOI 0507
BibRef

Rodner, E.[Erik], Denzler, J.[Joachim],
Learning with few examples for binary and multiclass classification using regularization of randomized trees,
PRL(32), No. 2, 15 January 2011, pp. 244-251.
Elsevier DOI 1101
BibRef
Earlier:
One-Shot Learning of Object Categories Using Dependent Gaussian Processes,
DAGM10(232-241).
Springer DOI 1009
BibRef
Earlier:
Randomized Probabilistic Latent Semantic Analysis for Scene Recognition,
CIARP09(945-953).
Springer DOI 0911
BibRef
Earlier:
Learning with Few Examples by Transferring Feature Relevance,
DAGM09(252-261).
Springer DOI 0909
Feature relevance from related tasks. Use as prior distribution. Object categorization; Randomized trees; Few examples; Interclass transfer; Transfer learning BibRef

Haase, D.[Daniel], Rodner, E.[Erid], Denzler, J.[Joachim],
Instance-Weighted Transfer Learning of Active Appearance Models,
CVPR14(1426-1433)
IEEE DOI 1409
active appearance models BibRef

Rahman, S.[Shafin], Khan, S.[Salman], Porikli, F.M.[Fatih M.],
A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning,
IP(27), No. 11, November 2018, pp. 5652-5667.
IEEE DOI 1809
Semantics, Visualization, Cats, Rats, Seals, Measurement, Task analysis, Zero-shot learning, few-shot learning, class adaptive principal direction BibRef

Rahman, S.[Shafin], Khan, S.[Salman], Porikli, F.M.[Fatih M.],
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts,
ACCV18(I:547-563).
Springer DOI 1906
BibRef

Zheng, Y.[Yan], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Xue, L.X.[Li-Xia], Hu, M.[Min],
Principal characteristic networks for few-shot learning,
JVCIR(59), 2019, pp. 563-573.
Elsevier DOI 1903
Few-shot learning, Principal characteristic, Mixture loss function, Embedding network, Fine-tuning BibRef

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

Mai, S.[Sijie], Hu, H.F.[Hai-Feng], Xu, J.[Jia],
Attentive matching network for few-shot learning,
CVIU(187), 2019, pp. 102781.
Elsevier DOI 1909
Few-shot learning, Metric learning, Feature attention, Complementary Cosine loss BibRef

Ding, Y.M.[Yue-Ming], Tian, X.[Xia], Yin, L.R.[Li-Rong], Chen, X.B.[Xia-Bing], Liu, S.[Shan], Yang, B.[Bo], Zheng, W.F.[Wen-Feng],
Multi-scale Relation Network for Few-shot Learning Based on Meta-learning,
CVS19(343-352).
Springer DOI 1912
BibRef

Chen, X., Wang, Y., Liu, J., Qiao, Y.,
DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection,
IP(29), 2020, pp. 7765-7778.
IEEE DOI 2007
Object detection, low-shot learning, continuous learning, deep learning, transfer learning BibRef

Zhang, C.J.[Chun-Jie], Li, C.H.[Cheng-Hua], Cheng, J.[Jian],
Few-Shot Visual Classification Using Image Pairs With Binary Transformation,
CirSysVideo(30), No. 9, September 2020, pp. 2867-2871.
IEEE DOI 2009
Training, Visualization, Testing, Correlation, Image representation, Automation, Convolutional neural networks, object categorization BibRef

Ji, Z.[Zhong], Chai, X.L.[Xing-Liang], Yu, Y.L.[Yun-Long], Pang, Y.W.[Yan-Wei], Zhang, Z.F.[Zhong-Fei],
Improved prototypical networks for few-Shot learning,
PRL(140), 2020, pp. 81-87.
Elsevier DOI 2012
Image classification, Attention network, Few-Shot learning, Metric learning 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, P.[Pei], Bai, Y.P.[Yun-Peng], Wang, D.[Dong], Bai, B.[Bendu], Li, Y.[Ying],
Few-Shot Classification of Aerial Scene Images via Meta-Learning,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zhu, W.[Wei], Li, W.B.[Wen-Bin], Liao, H.[Haofu], Luo, J.B.[Jie-Bo],
Temperature network for few-shot learning with distribution-aware large-margin metric,
PR(112), 2021, pp. 107797.
Elsevier DOI 2102
Few-shot learning, Metric learning, Skin lesion classification, Temperature function BibRef

Song, Y.[Yu], Chen, C.S.[Chang-Sheng],
MPPCANet: A Feedforward Learning Strategy for Few-Shot Image Classification,
PR(113), 2021, pp. 107792.
Elsevier DOI 2103
Feedforward learning, PCANet, Mixtures of probabilistic principal component analysis BibRef

Zhu, Y.H.[Yao-Hui], Min, W.Q.[Wei-Qing], Jiang, S.Q.[Shu-Qiang],
Attribute-Guided Feature Learning for Few-Shot Image Recognition,
MultMed(23), 2021, pp. 1200-1209.
IEEE DOI 2105
Image recognition, Training, Task analysis, Semantics, Standards, Measurement, Visualization, Attribute learning, few-shot learning, image recognition BibRef

Xu, H.[Hui], Wang, J.X.[Jia-Xing], Li, H.[Hao], Ouyang, D.Q.[De-Qiang], Shao, J.[Jie],
Unsupervised meta-learning for few-shot learning,
PR(116), 2021, pp. 107951.
Elsevier DOI 2106
Unsupervised learning, Meta-learning, Few-shot learning BibRef

Huang, H.X.[Hua-Xi], Zhang, J.J.[Jun-Jie], Zhang, J.[Jian], Xu, J.S.[Jing-Song], Wu, Q.[Qiang],
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification,
MultMed(23), 2021, pp. 1666-1680.
IEEE DOI 2106
Feature extraction, Task analysis, Data models, Dogs, Covariance matrices, Neural networks, Training, Bilinear pooling, pairwise BibRef

Liu, G.[Ge], Zhao, L.[Linglan], Fang, X.Z.[Xiang-Zhong],
PDA: Proxy-based domain adaptation for few-shot image recognition,
IVC(110), 2021, pp. 104164.
Elsevier DOI 2106
Few-shot image recognition, Domain adaptation, Few-shot learning, Transfer learning BibRef

Huang, H.W.[Hong-Wei], Wu, Z.[Zhangkai], Li, W.B.[Wen-Bin], Huo, J.[Jing], Gao, Y.[Yang],
Local descriptor-based multi-prototype network for few-shot Learning,
PR(116), 2021, pp. 107935.
Elsevier DOI 2106
Few-shot learning, Image classification, Local descriptors, Multiple prototypes, End-to-end learning BibRef

Ye, H.J.[Han-Jia], Hum, H.X.[He-Xiang], Zhan, D.C.[De-Chuan],
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning,
IJCV(129), No. 6, June 2021, pp. 1930-1953.
Springer DOI 2106
BibRef

Zhang, B.Q.[Bao-Quan], Leung, K.C.[Ka-Cheong], Li, X.T.[Xu-Tao], Ye, Y.M.[Yun-Ming],
Learn to abstract via concept graph for weakly-supervised few-shot learning,
PR(117), 2021, pp. 107946.
Elsevier DOI 2106
Few-shot learning, Weakly-supervised learning, Meta-learning, Concept graph BibRef

Kim, J.[Joseph], Chi, M.M.[Ming-Min],
SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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

Zeng, Q.J.[Qing-Jie], Geng, J.[Jie], Huang, K.[Kai], Jiang, W.[Wen], Guo, J.[Jun],
Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Jiang, N.[Nan], Shi, H.[Haowen], Geng, J.[Jie],
Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zeng, Q.J.[Qing-Jie], Geng, J.[Jie],
Task-specific contrastive learning for few-shot remote sensing image scene classification,
PandRS(191), 2022, pp. 143-154.
Elsevier DOI 2208
Remote sensing image, Few-shot learning, Scene classification, Contrastive learning BibRef

Li, Y.[Yong], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Cai, B.[Bowen], Peng, S.[Song],
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Li, H.F.[Hai-Feng], Cui, Z.Q.[Zhen-Qi], Zhu, Z.Q.[Zhi-Qiang], Chen, L.[Li], Zhu, J.W.[Jia-Wei], Huang, H.Z.[Hao-Zhe], Tao, C.[Chao],
RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification,
GeoRS(59), No. 8, August 2021, pp. 6983-6994.
IEEE DOI 2108
Task analysis, Remote sensing, Measurement, Training, Neural networks, Feature extraction, Data models, remote sensing classification BibRef

Doveh, S.[Sivan], Schwartz, E.[Eli], Xue, C.[Chao], Feris, R.[Rogerio], Bronstein, A.M.[Alex M.], Giryes, R.[Raja], Karlinsky, L.[Leonid],
MetAdapt: Meta-learned task-adaptive architecture for few-shot classification,
PRL(149), 2021, pp. 130-136.
Elsevier DOI 2108
BibRef

Chen, X.Y.[Xiang-Yu], Wang, G.H.[Guang-Hui],
Few-Shot Learning by Integrating Spatial and Frequency Representation,
CRV21(49-56)
IEEE DOI 2108
Machine learning algorithms, Frequency-domain analysis, Machine learning, Classification algorithms, frequency information BibRef

Singh, R.[Rishav], Bharti, V.[Vandana], Purohit, V.[Vishal], Kumar, A.[Abhinav], Singh, A.K.[Amit Kumar], Singh, S.K.[Sanjay Kumar],
MetaMed: Few-shot medical image classification using gradient-based meta-learning,
PR(120), 2021, pp. 108111.
Elsevier DOI 2109
Few-shot learning, Meta-learning, Multi-shot learning, Medical image classification, Image augmentation, Histopathological image classification BibRef

Li, X.Z.[Xin-Zhe], Huang, J.Q.[Jian-Qiang], Liu, Y.Y.[Yao-Yao], Zhou, Q.[Qin], Zheng, S.[Shibao], Schiele, B.[Bernt], Sun, Q.R.[Qian-Ru],
Learning to teach and learn for semi-supervised few-shot image classification,
CVIU(212), 2021, pp. 103270.
Elsevier DOI 2110
Few-shot learning, Meta-learning, Semi-supervised learning BibRef

Gong, H.Y.[Hui-Yun], Wang, S.[Shuo], Zhao, X.W.[Xiao-Wei], Yan, Y.F.[Yi-Fan], Ma, Y.Q.[Yu-Qing], Liu, W.[Wei], Liu, X.L.[Xiang-Long],
Few-shot learning with relation propagation and constraint,
IET-CV(15), No. 8, 2021, pp. 608-617.
DOI Link 2110
correlation methods, graph theory, image recognition BibRef

Hu, Z.P.[Zheng-Ping], Li, Z.J.[Zi-Jun], Wang, X.[Xueyu], Zheng, S.[Saiyue],
Unsupervised descriptor selection based meta-learning networks for few-shot classification,
PR(122), 2022, pp. 108304.
Elsevier DOI 2112
Meta-learning, Few-shot classification, Unsupervised localization, Descriptor selection BibRef

Hu, Y.F.[Yu-Fan], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Dual-Pooling Graph Neural Networks for Few-Shot Video Classification,
MultMed(23), 2021, pp. 4285-4296.
IEEE DOI 2112
Task analysis, Feature extraction, Training, Testing, Streaming media, Data models, Semantics, Few-shot learning, video classification BibRef

Feng, Y.B.[Yang-Bo], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Dual-Routing Capsule Graph Neural Network for Few-Shot Video Classification,
MultMed(25), 2023, pp. 3204-3216.
IEEE DOI 2309
BibRef

Cui, Y.W.[Ya-Wen], Liao, Q.[Qing], Hu, D.[Dewen], An, W.[Wei], Liu, L.[Li],
Coarse-to-fine pseudo supervision guided meta-task optimization for few-shot object classification,
PR(122), 2022, pp. 108296.
Elsevier DOI 2112
Unsupervised few-shot learning, Meta-learning, Clustering, Object classification BibRef

Lin, C.C.[Chia-Ching], Chu, H.L.[Hsin-Li], Wang, Y.C.A.F.[Yu-Chi-Ang Frank], Lei, C.L.[Chin-Laung],
Joint Feature Disentanglement and Hallucination for Few-Shot Image Classification,
IP(30), 2021, pp. 9245-9258.
IEEE DOI 2112
Task analysis, Feature extraction, Visualization, Training, Data models, Data mining, Birds, Few-shot learning (FSL), feature disentanglement BibRef

Zhang, L.[Lei], Zuo, L.Y.[Li-Yun], Du, Y.J.[Ying-Jun], Zhen, X.T.[Xian-Tong],
Learning to Adapt With Memory for Probabilistic Few-Shot Learning,
CirSysVideo(31), No. 11, November 2021, pp. 4283-4292.
IEEE DOI 2112
Task analysis, Adaptation models, Probabilistic logic, Optimization, Neural networks, Prototypes, Predictive models, variational inference BibRef

Zhang, P.[Pei], Fan, G.L.[Guo-Liang], Wu, C.[Chanyue], Wang, D.[Dong], Li, Y.[Ying],
Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Label Independent Memory for Semi-Supervised Few-Shot Video Classification,
PAMI(44), No. 1, January 2022, pp. 273-285.
IEEE DOI 2112
BibRef
Earlier:
Compound Memory Networks for Few-Shot Video Classification,
ECCV18(VII: 782-797).
Springer DOI 1810
Training, Feature extraction, Task analysis, Compounds, Dynamics, Data models, Prototypes, Few-shot video classification, compound memory networks BibRef

Fu, K.[Kun], Zhang, T.F.[Teng-Fei], Zhang, Y.[Yue], Wang, Z.R.[Zhi-Rui], Sun, X.[Xian],
Few-Shot SAR Target Classification via Metalearning,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Task analysis, Synthetic aperture radar, Training, Target recognition, Adaptation models, Analytical models, synthetic aperture radar (SAR) 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

Huang, W.D.[Wen-Dong], Yuan, Z.W.[Zheng-Wu], Yang, A.X.[Ai-Xia], Tang, C.[Chan], Luo, X.B.[Xiao-Bo],
TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Yuan, Z.W.[Zheng-Wu], Huang, W.D.[Wen-Dong], Tang, C.[Chan], Yang, A.[Aixia], Luo, X.B.[Xiao-Bo],
Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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

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

Cao, C.Q.[Cong-Qi], Zhang, Y.N.[Yan-Ning],
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning,
IP(31), 2022, pp. 1462-1474.
IEEE DOI 2202
Measurement, Streaming media, Task analysis, Semantics, Mutual information, Uncertainty, Feature extraction, semantic alignment BibRef

Ji, Z.[Zhong], Hou, Z.S.[Zhi-Shen], Liu, X.[Xiyao], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong],
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning,
IP(31), 2022, pp. 1520-1531.
IEEE DOI 2202
Semantics, Visualization, Task analysis, Training, Correlation, Sun, Learning systems, Few-shot learning, meta-learning, multi-modal, graph propagation BibRef

Wu, S.[Shuang], Kankanhalli, M.S.[Mohan S.], Tung, A.K.H.[Anthony K.H.],
Superclass-aware network for few-shot learning,
CVIU(216), 2022, pp. 103349.
Elsevier DOI 2202
Few-shot learning, Contrastive loss, Feature attention BibRef

Cheng, J.[Jun], Hao, F.S.[Fu-Sheng], Liu, L.[Liu], Tao, D.C.[Da-Cheng],
Imposing Semantic Consistency of Local Descriptors for Few-Shot Learning,
IP(31), 2022, pp. 1587-1600.
IEEE DOI 2202
Semantics, Training, Training data, Task analysis, Convolutional neural networks, Adaptation models, semantic consistency BibRef

Hao, F.S.[Fu-Sheng], He, F.X.[Feng-Xiang], Cheng, J.[Jun], Tao, D.C.[Da-Cheng],
Global-Local Interplay in Semantic Alignment for Few-Shot Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4351-4363.
IEEE DOI 2207
Semantics, Feature extraction, Measurement, Training, Learning systems, Visualization, Cats, Few-shot learning, global-local interplay BibRef

Hao, F.S.[Fu-Sheng], He, F.X.[Feng-Xiang], Cheng, J.[Jun], Wang, L., Cao, J., Tao, D.C.[Da-Cheng],
Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning,
ICCV19(8459-8468)
IEEE DOI 2004
Code, Metric Learning.
WWW Link. image retrieval, learning (artificial intelligence), multilayer perceptrons, tensors, 3D tensor, Task analysis BibRef

Li, F.[Feimo], Li, S.B.[Shuai-Bo], Fan, X.X.[Xin-Xin], Li, X.[Xiong], Chang, H.X.[Hong-Xing],
Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhang, B.[Bo], Ye, H.C.[Han-Cheng], Yu, G.[Gang], Wang, B.[Bin], Wu, Y.[Yike], Fan, J.Y.[Jia-Yuan], Chen, T.[Tao],
Sample-Centric Feature Generation for Semi-Supervised Few-Shot Learning,
IP(31), 2022, pp. 2309-2320.
IEEE DOI 2203
Task analysis, Data models, Measurement, Training, Semantics, Adaptation models, Benchmark testing, Few-shot learning, sample-centric BibRef

Zhang, L.L.[Ling-Ling], Wang, S.W.[Shao-Wei], Chang, X.J.[Xiao-Jun], Liu, J.[Jun], Ge, Z.Y.[Zong-Yuan], Zheng, Q.H.[Qing-Hua],
Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning,
CirSysVideo(32), No. 3, March 2022, pp. 1213-1223.
IEEE DOI 2203
Training, Task analysis, Visualization, Search problems, Neural networks, Network architecture, DARTS 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

Liang, M.J.[Ming-Jiang], Huang, S.L.[Shao-Li], Pan, S.R.[Shi-Rui], Gong, M.M.[Ming-Ming], Liu, W.[Wei],
Learning multi-level weight-centric features for few-shot learning,
PR(128), 2022, pp. 108662.
Elsevier DOI 2205
Fewshot learning, Low-shot learning, Multi-level features, Image classification BibRef

Fu, W.[Wen], Zhou, L.[Li], Chen, J.[Jie],
Bidirectional Matching Prototypical Network for Few-Shot Image Classification,
SPLetters(29), 2022, pp. 982-986.
IEEE DOI 2205
Prototypes, Training, Feature extraction, Image classification, Task analysis, Predictive models, Measurement, metric-based method BibRef

Huang, J.[Jing], Wu, B.[Bin], Li, P.[Peng], Li, X.[Xiao], Wang, J.[Jie],
Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Huang, J.[Jing], Li, X.[Xiao], Wu, B.[Bin], Wu, X.Y.[Xin-Yu], Li, P.[Peng],
Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Cai, J.L.[Jin-Lei], Zhang, Y.T.[Yue-Ting], Guo, J.Y.[Jia-Yi], Zhao, X.[Xin], Lv, J.W.[Jun-Wei], Hu, Y.X.[Yu-Xin],
ST-PN: A Spatial Transformed Prototypical Network for Few-Shot SAR Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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

Zhou, Y.[Yuan], Guo, Y.R.[Yan-Rong], Hao, S.J.[Shi-Jie], Hong, R.C.[Ri-Chang],
Hierarchical Prototype Refinement With Progressive Inter-Categorical Discrimination Maximization for Few-Shot Learning,
IP(31), 2022, pp. 3414-3429.
IEEE DOI 2205
Prototypes, Training, Semantics, Visualization, Task analysis, Interference, Correlation, Few-shot learning, metric learning, inter-categorical discrimination BibRef

Wang, J.Y.[Jia-Yan], Wang, X.Q.[Xue-Qin], Xing, L.[Lei], Liu, B.D.[Bao-Di], Li, Z.[Zongmin],
Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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

Wang, Y.N.[Ya-Ning], Liu, Z.J.[Zi-Jian], Luo, Y.[Yang], Luo, C.[Chunbo],
A transductive learning method to leverage graph structure for few-shot learning,
PRL(159), 2022, pp. 189-195.
Elsevier DOI 2206
few-shot learning, clustering, semi-supervised learning, graph neural networks BibRef

Chen, H.X.[Hao-Xing], Li, H.X.[Hua-Xiong], Li, Y.[Yaohui], Chen, C.L.[Chun-Lin],
Shaping Visual Representations With Attributes for Few-Shot Recognition,
SPLetters(29), 2022, pp. 1397-1401.
IEEE DOI 2207
Visualization, Training, Semantics, Prototypes, Task analysis, Sun, Representation learning, Attribute-shaped learning, attribute-visual attention BibRef

Guo, Y.R.[Yu-Rong], Du, R.[Ruoyi], Li, X.X.[Xiao-Xu], Xie, J.Y.[Ji-Yang], Ma, Z.Y.[Zhan-Yu], Dong, Y.[Yuan],
Learning Calibrated Class Centers for Few-Shot Classification by Pair-Wise Similarity,
IP(31), 2022, pp. 4543-4555.
IEEE DOI 2207
Semantics, Measurement, Feature extraction, Correlation, Training, Strain, Learning systems, Few-shot image classification, query-guided mask 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

Schwartz, E.[Eli], Karlinsky, L.[Leonid], Feris, R.[Rogerio], Giryes, R.[Raja], Bronstein, A.[Alex],
Baby steps towards few-shot learning with multiple semantics,
PRL(160), 2022, pp. 142-147.
Elsevier DOI 2208
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

Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Li, Y.S.[Yun-Song], Song, R.[Rui], Hong, D.F.[Dan-Feng], Chanussot, J.[Jocelyn],
Few-Shot Learning With Class-Covariance Metric for Hyperspectral Image Classification,
IP(31), 2022, pp. 5079-5092.
IEEE DOI 2208
Measurement, Training, Task analysis, Euclidean distance, Feature extraction, Iron, Hyperspectral imaging, Few-shot learning, HSI classification BibRef

Shao, S.[Shuai], Xing, L.[Lei], Xu, R.[Rui], Liu, W.F.[Wei-Feng], Wang, Y.J.[Yan-Jiang], Liu, B.D.[Bao-Di],
MDFM: Multi-Decision Fusing Model for Few-Shot Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5151-5162.
IEEE DOI 2208
Feature extraction, Finite element analysis, Fuses, Dogs, Data models, Birds, Adaptation models, Few-shot learning (FSL), multi-decision fusing model (MDFM) BibRef

Wu, J.Y.[Jia-Ying], Hu, J.L.[Jing-Lu],
Redefining prior feature space via finetuning a triplet network for few-shot learning,
IET-CV(16), No. 6, 2022, pp. 514-524.
DOI Link 2208
contrastive learning, few-shot learning, maximum a posteriori, pretrained feature extractor, triplet network BibRef

Wang, Y.K.[Yi-Kai], Zhang, L.[Li], Yao, Y.[Yuan], Fu, Y.W.[Yan-Wei],
How to Trust Unlabeled Data? Instance Credibility Inference for Few-Shot Learning,
PAMI(44), No. 10, October 2022, pp. 6240-6253.
IEEE DOI 2209
Training, Data models, Noise measurement, Task analysis, Feature extraction, Visualization, Standards, Few-shot learning, self-taught learning BibRef

Wang, Y.K.[Yi-Kai], Xu, C.M.[Cheng-Ming], Liu, C.[Chen], Zhang, L.[Li], Fu, Y.W.[Yan-Wei],
Instance Credibility Inference for Few-Shot Learning,
CVPR20(12833-12842)
IEEE DOI 2008
Training, Data models, Feature extraction, Prediction algorithms, Training data, Linear regression, Semisupervised learning BibRef

Gao, F.[Fei], Xu, J.M.[Jing-Ming], Lang, R.L.[Rong-Ling], Wang, J.[Jun], Hussain, A.[Amir], Zhou, H.Y.[Hui-Yu],
A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Zhang, J.[Jing], Zhang, X.Z.[Xin-Zhou], Wang, Z.[Zhe],
Task Encoding With Distribution Calibration for Few-Shot Learning,
CirSysVideo(32), No. 9, September 2022, pp. 6240-6252.
IEEE DOI 2209
Task analysis, Feature extraction, Adaptation models, Calibration, Encoding, Computational modeling, Training, Few-shot learning, image classification BibRef

Zhou, F.[Fei], Zhang, L.[Lei], Wei, W.[Wei],
Meta-Generating Deep Attentive Metric for Few-Shot Classification,
CirSysVideo(32), No. 10, October 2022, pp. 6863-6873.
IEEE DOI 2210
Measurement, Task analysis, Training, Gaussian distribution, Optimization, Standards, Feature extraction, Few-shot learning, meta-learning BibRef

Li, Z.J.[Zi-Jun], Hu, Z.P.[Zheng-Ping], Luo, W.W.[Wei-Wei], Hu, X.[Xiao],
SaberNet: Self-attention based effective relation network for few-shot learning,
PR(133), 2023, pp. 109024.
Elsevier DOI 2210
Few-shot learning, Feature representation, Task analysis, Transformers BibRef

Yang, S.[Sai], Liu, F.[Fan], Chen, Z.[Zhiyu],
Feature hallucination in hypersphere space for few-shot classification,
IET-IPR(16), No. 13, 2022, pp. 3603-3616.
DOI Link 2210
BibRef

Yang, S.[Shuo], Wu, S.H.[Song-Hua], Liu, T.L.[Tong-Liang], Xu, M.[Min],
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration,
PAMI(44), No. 12, December 2022, pp. 9830-9843.
IEEE DOI 2212
Data models, Task analysis, Training, Mathematical models, Calibration, Adaptation models, Gaussian distribution, generalization error 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

Huang, X.L.[Xi-Lang], Choi, S.H.[Seon Han],
SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning,
PR(135), 2023, pp. 109170.
Elsevier DOI 2212
Few-shot learning, Multi-head self-attention mechanism, Image classification, -Nearest neighbor BibRef

Xu, R.[Rui], Xing, L.[Lei], Shao, S.[Shuai], Zhao, L.F.[Li-Fei], Liu, B.[Baodi], Liu, W.F.[Wei-Feng], Zhou, Y.C.[Yi-Cong],
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8674-8687.
IEEE DOI 2212
Feature extraction, Data mining, Finite element analysis, Training data, Semi-supervised learning, Few-shot learning, graph co-training (GCT) BibRef

Cui, Z.[Zhiyan], Lu, N.[Na], Wang, W.F.[Wei-Feng], Guo, G.S.[Guang-Shuai],
Dual global-aware propagation for few-shot learning,
IVC(128), 2022, pp. 104574.
Elsevier DOI 2212
Few-shot learning, Label propagation, Global-aware features, Feature fusion BibRef

Jiang, S.Q.[Shu-Qiang], Zhu, Y.[Yaohui], Liu, C.L.[Chen-Long], Song, X.H.[Xin-Hang], Li, X.Y.[Xiang-Yang], Min, W.Q.[Wei-Qing],
Dataset Bias in Few-Shot Image Recognition,
PAMI(45), No. 1, January 2023, pp. 229-246.
IEEE DOI 2212
Task analysis, Visualization, Learning systems, Adaptation models, Image recognition, Training, Complexity theory, Dataset bias, meta-learning BibRef

Wu, J.Y.[Jia-Ying], Hu, J.L.[Jing-Lu],
Learning a Latent Space with Triplet Network for Few-Shot Image Classification,
ICPR22(5038-5044)
IEEE DOI 2212
Training data, Benchmark testing, Feature extraction, Task analysis, Image classification BibRef

Wang, R.Q.[Run-Qi], Liu, Z.[Zhen], Zhang, B.C.[Bao-Chang], Guo, G.D.[Guo-Dong], Doermann, D.[David],
Few-Shot Learning with Complex-Valued Neural Networks and Dependable Learning,
IJCV(131), No. 1, January 2023, pp. 385-404.
Springer DOI 2301
BibRef

Xu, J.[Jian], Liu, B.[Bo], Xiao, Y.[Yanshan],
A Variational Inference Method for Few-Shot Learning,
CirSysVideo(33), No. 1, January 2023, pp. 269-282.
IEEE DOI 2301
Task analysis, Power capacitors, Estimation, Image synthesis, Feature extraction, Training, Neural networks, variational autoencoder (VAE) BibRef

Zhang, L.[Lei], Zhou, F.[Fei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Meta-hallucinating prototype for few-shot learning promotion,
PR(136), 2023, pp. 109235.
Elsevier DOI 2301
Few-shot learning, Prototype hallucination, Meta-learning BibRef

Cheng, J.[Jun], Hao, F.[Fusheng], He, F.X.[Feng-Xiang], Liu, L.[Liu], Zhang, Q.[Qieshi],
Mixer-Based Semantic Spread for Few-Shot Learning,
MultMed(25), 2023, pp. 191-202.
IEEE DOI 2301
Semantics, Feature extraction, Training, Mixers, Task analysis, Visualization, Few-shot learning, metric learning-based meta-learning BibRef

Ye, H.J.[Han-Jia], Han, L.[Lu], Zhan, D.C.[De-Chuan],
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks,
PAMI(45), No. 3, March 2023, pp. 3721-3737.
IEEE DOI 2302
Task analysis, Unified modeling language, Training, Feature extraction, Semantics, Labeling, Visualization, self-supervised learning 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

Wang, J.W.[Jun-Wen], Gao, Y.B.[Yong-Bin], Fang, Z.J.[Zhi-Jun],
An angular shrinkage BERT model for few-shot relation extraction with none-of-the-above detection,
PRL(166), 2023, pp. 151-158.
Elsevier DOI 2302
Few-shot learning, Relation extraction, None-of-the-above detection BibRef

Liu, X.Y.[Xin-Yue], Liu, L.G.[Li-Gang], Liu, H.[Han], Zhang, X.T.[Xiao-Tong],
Capturing the few-shot class distribution: Transductive distribution optimization,
PR(138), 2023, pp. 109371.
Elsevier DOI 2303
Few-shot learning, Transductive learning, Distribution estimation BibRef

Liu, F.[Fan], Li, F.F.[Fei-Fan], Yang, S.[Sai],
Few-shot classification using Gaussianisation prototypical classifier,
IET-CV(17), No. 1, 2023, pp. 62-75.
DOI Link 2303
few-shot classification, maximum a posteriori, reliable prototype 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

Li, W.B.[Wen-Bin], Wang, L.[Lei], Zhang, X.X.[Xing-Xing], Qi, L.[Lei], Huo, J.[Jing], Gao, Y.[Yang], Luo, J.B.[Jie-Bo],
Defensive Few-Shot Learning,
PAMI(45), No. 5, May 2023, pp. 5649-5667.
IEEE DOI 2304
Training, Task analysis, Image classification, Robustness, Convolutional neural networks, Learning systems, episodic training BibRef

Qiang, W.W.[Wen-Wen], Li, J.M.[Jiang-Meng], Su, B.[Bing], Fu, J.L.[Jian-Long], Xiong, H.[Hui], Wen, J.R.[Ji-Rong],
Meta Attention-Generation Network for Cross-Granularity Few-Shot Learning,
IJCV(131), No. 5, May 2023, pp. 1211-1233.
Springer DOI 2305
BibRef

Tabealhojeh, H.[Hadi], Adibi, P.[Peyman], Karshenas, H.[Hossein], Roy, S.K.[Soumava Kumar], Harandi, M.[Mehrtash],
RMAML: Riemannian meta-learning with orthogonality constraints,
PR(140), 2023, pp. 109563.
Elsevier DOI 2305
Meta-learning, Geometry-aware optimization, Riemannian manifolds, Few-shot image classification BibRef

Zhao, Y.Q.[Yun-Qing], Cheung, N.M.[Ngai-Man],
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification,
IP(32), 2023, pp. 2252-2266.
IEEE DOI 2305
Training, Power capacitors, Task analysis, Data models, Knowledge engineering, Adaptation models, Training data, meta-learning BibRef

Vu, A.K.N.[Anh-Khoa Nguyen], Do, T.T.[Thanh-Toan], Nguyen, N.D.[Nhat-Duy], Nguyen, V.T.[Vinh-Tiep], Ngo, T.D.[Thanh Duc], Nguyen, T.V.[Tam V.],
Instance-Level Few-Shot Learning With Class Hierarchy Mining,
IP(32), 2023, pp. 2374-2385.
IEEE DOI 2305
Feature extraction, Training, Data mining, Training data, Task analysis, Proposals, Object detection, Few-shot learning, hierarchical information BibRef

Shao, S.[Shuai], Xing, L.[Lei], Wang, Y.J.[Yan-Jiang], Liu, B.[Baodi], Liu, W.F.[Wei-Feng], Zhou, Y.C.[Yi-Cong],
Attention-Based Multi-View Feature Collaboration for Decoupled Few-Shot Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2357-2369.
IEEE DOI 2305
Collaboration, Feature extraction, Finite element analysis, Task analysis, Training, Learning systems, Data models, self-attention block BibRef

Xu, C.M.[Cheng-Ming], Liu, C.[Chen], Sun, X.W.[Xin-Wei], Yang, S.[Siqian], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie], Fu, Y.W.[Yan-Wei],
PatchMix Augmentation to Identify Causal Features in Few-Shot Learning,
PAMI(45), No. 6, June 2023, pp. 7639-7653.
IEEE DOI 2305
Correlation, Training, Dogs, Data models, Task analysis, Image reconstruction, Training data, Few-shot learning, intra-variance regularization BibRef

Pan, M.H.[Mei-Hong], Xin, H.Y.[Hong-Yi], Xia, C.Q.[Chun-Qiu], Shen, H.B.[Hong-Bin],
Few-shot classification with task-adaptive semantic feature learning,
PR(141), 2023, pp. 109594.
Elsevier DOI 2306
Few-shot learning, Multi-modality, Task-adaptive training, Semantic feature learner 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

Zhang, H.G.[Hong-Guang], Li, H.D.[Hong-Dong], Koniusz, P.[Piotr],
Multi-Level Second-Order Few-Shot Learning,
MultMed(25), 2023, pp. 2111-2126.
IEEE DOI 2306
BibRef
Earlier: A1, A3, Only:
Power Normalizing Second-Order Similarity Network for Few-Shot Learning,
WACV19(1185-1193)
IEEE DOI 1904
Task analysis, Pipelines, Image recognition, Visualization, Feature extraction, Training, Streaming media, Few-shot learning, action recognition. higher order statistics, image capture, learning (artificial intelligence), protocols BibRef

Zhang, B.Q.[Bao-Quan], Jiang, H.[Hao], Li, X.[Xutao], Feng, S.S.[Shan-Shan], Ye, Y.M.[Yun-Ming], Luo, C.[Chen], Ye, R.[Rui],
MetaDT: Meta Decision Tree With Class Hierarchy for Interpretable Few-Shot Learning,
CirSysVideo(33), No. 6, June 2023, pp. 2826-2838.
IEEE DOI 2306
Decision trees, Visualization, Dogs, Task analysis, Semantics, Neural networks, Heating systems, Few-shot learning, meta-learning, class hierarchy BibRef

Tan, Q.[Qi], Wu, Z.[Zongze], Lai, J.[Jialun], Liang, Z.X.[Ze-Xiao], Ren, Z.G.[Zhi-Gang],
HDGN: Heat diffusion graph network for few-shot learning,
PRL(171), 2023, pp. 61-68.
Elsevier DOI 2306
Few-shot learning, Graph convolution network, Low-pass filter, Heat diffusion, Gait recognition, Image entropy, Multi-view recognition BibRef

Shi, B.[Boyao], Li, W.B.[Wen-Bin], Huo, J.[Jing], Zhu, P.F.[Peng-Fei], Wang, L.[Lei], Gao, Y.[Yang],
Global- and local-aware feature augmentation with semantic orthogonality for few-shot image classification,
PR(142), 2023, pp. 109702.
Elsevier DOI 2307
Few-shot image classification, Transfer learning, Feature augmentation, Semantic orthogonal learning BibRef

Zhang, M.[Min], Huang, S.[Siteng], Li, W.B.[Wen-Bin], Wang, D.L.[Dong-Lin],
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation,
ECCV22(XX:453-470).
Springer DOI 2211
BibRef

Chen, H.[Hao], Li, L.Y.[Lin-Yan], Hu, F.Y.[Fu-Yuan], Lyu, F.[Fan], Zhao, L.Q.[Liu-Qing], Huang, K.Z.[Kai-Zhu], Feng, W.[Wei], Xia, Z.P.[Zhen-Ping],
Multi-semantic hypergraph neural network for effective few-shot learning,
PR(142), 2023, pp. 109677.
Elsevier DOI 2307
Hypergraph, Few-shot learning, Multi-semantic learning, Orthogonal training BibRef

Chen, J.J.[Jing-Jing], Zhuo, L.H.[Lin-Hai], Wei, Z.P.[Zhi-Peng], Zhang, H.[Hao], Fu, H.Z.[Hua-Zhu], Jiang, Y.G.[Yu-Gang],
Knowledge driven weights estimation for large-scale few-shot image recognition,
PR(142), 2023, pp. 109668.
Elsevier DOI 2307
Few-shot image, Recognition, Knowledge transfer BibRef

Shao, Y.J.[Yuan-Jie], Wu, W.X.[Wen-Xiao], You, X.G.[Xin-Ge], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
Improving the Generalization of MAML in Few-Shot Classification via Bi-Level Constraint,
CirSysVideo(33), No. 7, July 2023, pp. 3284-3295.
IEEE DOI 2307
Adaptation models, Task analysis, Optimization, Measurement, Power capacitors, Feature extraction, Data models, MAML, cross-task metric loss 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

Peng, D.[Danni], Pan, S.J.L.[Sinno Jia-Lin],
Clustered Task-Aware Meta-Learning by Learning from Learning Paths,
PAMI(45), No. 8, August 2023, pp. 9426-9438.
IEEE DOI 2307

WWW Link. Task analysis, Training, Feature extraction, Modulation, Trajectory, Optimization, Knowledge engineering, Task clustering, task-aware meta-learning BibRef

Zha, Z.[Zican], Tang, H.[Hao], Sun, Y.[Yunlian], Tang, J.H.[Jin-Hui],
Boosting Few-Shot Fine-Grained Recognition With Background Suppression and Foreground Alignment,
CirSysVideo(33), No. 8, August 2023, pp. 3947-3961.
IEEE DOI 2308
Task analysis, Measurement, Feature extraction, Birds, Annotations, Training, Sun, Few-shot learning, fine-grained recognition, foreground alignment BibRef

Wang, S.M.[Shuang-Mei], Ma, R.[Rui], Wu, T.[Tieru], Cao, Y.[Yang],
P3DC-shot: Prior-driven discrete data calibration for nearest-neighbor few-shot classification,
IVC(136), 2023, pp. 104736.
Elsevier DOI 2308
Few-shot learning, Image classification, Prototype, Calibration BibRef

Hu, Z.X.[Zi-Xuan], Shen, L.[Li], Lai, S.[Shenqi], Yuan, C.[Chun],
Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot Classification,
CirSysVideo(33), No. 8, August 2023, pp. 3638-3648.
IEEE DOI 2308
Task analysis, Bayes methods, Frequency division multiplexing, Correlation, Uncertainty, Prototypes, Semantics, Bayesian inference BibRef

Dang, Z.H.[Zhuo-Hang], Luo, M.[Minnan], Jia, C.Y.[Cheng-You], Yan, C.X.[Cai-Xia], Chang, X.J.[Xiao-Jun], Zheng, Q.H.[Qing-Hua],
Counterfactual Generation Framework for Few-Shot Learning,
CirSysVideo(33), No. 8, August 2023, pp. 3747-3758.
IEEE DOI 2308
Feature extraction, Data models, Task analysis, Prototypes, Data mining, Semantics, Generators, Few-shot learning, prototype learning BibRef

Song, Y.S.[Yi-Sheng], Wang, T.[Ting], Cai, P.[Puyu], Mondal, S.K.[Subrota K.], Sahoo, J.P.[Jyoti Prakash],
A Comprehensive Survey of Few-Shot Learning: Evolution, Applications, Challenges, and Opportunities,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link 2309
Survey, Few-Shot Learning. prior knowledge, meta-learning, low-shot learning, zero-shot learning, one-shot learning, Few-shot learning BibRef

Cao, J.Z.[Jiang-Zhong], Yao, Z.J.[Zi-Jie], Yu, L.G.[Liang-Geng], Ling, B.W.K.[Bingo Wing-Kuen],
WPE: Weighted prototype estimation for few-shot learning,
IVC(137), 2023, pp. 104757.
Elsevier DOI 2309
Few-shot learning, Knowledge transfer, Data augmentation, Prototype estimation, Image classification BibRef

Wu, Y.Q.[Ya-Qiang], Li, Y.F.[Yi-Fei], Zhao, T.Z.[Tian-Zhe], Zhang, L.L.[Ling-Ling], Wei, B.[Bifan], Liu, J.[Jun], Zheng, Q.H.[Qing-Hua],
Improved prototypical network for active few-shot learning,
PRL(172), 2023, pp. 188-194.
Elsevier DOI 2309
Few-shot learning, Active learning, Prototypical network, Loss prediction, Image recognition BibRef

Song, K.[Kun], Wu, Y.C.[Yu-Chen], Chen, J.S.[Jian-Sheng], Hu, T.Y.[Tian-Yu], Ma, H.M.[Hui-Min],
Gestalt-guided Image Understanding for Few-shot Learning,
ACCV22(II:409-424).
Springer DOI 2307
BibRef

Sendera, M.[Marcin], Przewiezlikowski, M.[Marcin], Karanowski, K.[Konrad], Zieba, M.[Maciej], Tabor, J.[Jacek], Spurek, P.[Przemyslaw],
HyperShot: Few-Shot Learning by Kernel HyperNetworks,
WACV23(2468-2477)
IEEE DOI 2302
Training, Adaptation models, Computational modeling, Switches, Predictive models, Planning, and algorithms (including transfer) BibRef

He, J.[Ju], Kortylewski, A.[Adam], Yuille, A.L.[Alan L.],
CORL: Compositional Representation Learning for Few-Shot Classification,
WACV23(3879-3888)
IEEE DOI 2302
Training, Representation learning, Dictionaries, Image recognition, Knowledge based systems, Neural networks, and algorithms (including transfer) BibRef

Subramanyam, R.[Rakshith], Heimann, M.[Mark], Jayram, T.S., Anirudh, R.[Rushil], Thiagarajan, J.J.[Jayaraman J.],
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification,
WACV23(2478-2486)
IEEE DOI 2302
Aggregates, Semantics, Prototypes, Modulation, Benchmark testing, Encoding, Algorithms: Machine learning architectures, visual reasoning BibRef

He, X.[Xi], Li, F.Z.[Fan-Zhang],
Task-adaptive Few-shot Learning on Sphere Manifold,
ICPR22(2949-2956)
IEEE DOI 2212
Manifolds, Learning systems, Technological innovation, Euclidean distance, Benchmark testing, Pattern recognition BibRef

Ma, Y.X.[Yi-Xiao], Li, F.Z.[Fan-Zhang],
Self-Challenging Mask for Cross-Domain Few-Shot Classification,
ICPR22(4456-4453)
IEEE DOI 2212
Measurement, Visualization, Analytical models, Feature extraction, Robustness, Power capacitors BibRef

Lu, Y.N.[Yu-Ning], Wen, L.J.[Liang-Jian], Liu, J.Z.[Jian-Zhuang], Liu, Y.J.[Ya-Jing], Tian, X.[Xinmei],
Self-Supervision Can Be a Good Few-Shot Learner,
ECCV22(XIX:740-758).
Springer DOI 2211
BibRef

Zhang, T.[Tao], Huang, W.[Wu],
Kernel Relative-prototype Spectral Filtering for Few-Shot Learning,
ECCV22(XX:541-557).
Springer DOI 2211
BibRef

Nguyen, K.D.[Khoi D.], Tran, Q.H.[Quoc-Huy], Nguyen, K.[Khoi], Hua, B.S.[Binh-Son], Nguyen, R.[Rang],
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments,
ECCV22(XX:471-487).
Springer DOI 2211
BibRef

Chen, W.T.[Wen-Tao], Zhang, Z.[Zhang], Wang, W.[Wei], Wang, L.[Liang], Wang, Z.[Zilei], Tan, T.N.[Tie-Niu],
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations,
ECCV22(XX:383-399).
Springer DOI 2211
BibRef

Dong, B.[Bowen], Zhou, P.[Pan], Yan, S.C.[Shui-Cheng], Zuo, W.M.[Wang-Meng],
Self-Promoted Supervision for Few-Shot Transformer,
ECCV22(XX:329-347).
Springer DOI 2211
BibRef

Yang, Z.Y.[Zhan-Yuan], Wang, J.H.[Jing-Hua], Zhu, Y.Y.[Ying-Ying],
Few-Shot Classification with Contrastive Learning,
ECCV22(XX:293-309).
Springer DOI 2211
BibRef

Li, H.Q.[Hao-Quan], Zhang, L.[Laoming], Zhang, D.[Daoan], Fu, L.[Lang], Yang, P.[Peng], Zhang, J.G.[Jian-Guo],
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning,
ECCV22(XX:524-540).
Springer DOI 2211
BibRef

Xiang, X.[Xiang], Tan, Y.[Yuwen], Wan, Q.[Qian], Ma, J.[Jing], Yuille, A.L.[Alan L.], Hager, G.D.[Gregory D.],
Coarse-To-Fine Incremental Few-Shot Learning,
ECCV22(XXXI:205-222).
Springer DOI 2211
BibRef

Li, S.[Shuo], Liu, F.[Fang], Hao, Z.[Zehua], Zhao, K.[Kaibo], Jiao, L.C.[Li-Cheng],
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space,
ECCV22(XXXI:420-436).
Springer DOI 2211
BibRef

Zhang, R.[Renrui], Zhang, W.[Wei], Fang, R.[Rongyao], Gao, P.[Peng], Li, K.C.[Kun-Chang], Dai, J.F.[Ji-Feng], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification,
ECCV22(XXXV:493-510).
Springer DOI 2211
BibRef

Rhee, H.C.[Ho-Chang], Cho, N.I.[Nam Ik],
Episode Difficulty Based Sampling Method for Few-Shot Classification,
ICIP22(296-300)
IEEE DOI 2211
Training, Codes, Benchmark testing, Sampling methods, Few-shot Learning, Episodic Training BibRef

Zarei, M.R.[Mohammad Reza], Komeili, M.[Majid],
Interpretable Concept-Based Prototypical Networks for Few-Shot Learning,
ICIP22(4078-4082)
IEEE DOI 2211
Annotations, Machine learning, Extraterrestrial measurements, Multitasking, Birds, Task analysis, Interpretability, Few-shot, Concept BibRef

Shirekar, O.K.[Ojas Kishore], Jamali-Rad, H.[Hadi],
Self-Supervised Class-Cognizant Few-Shot Classification,
ICIP22(976-980)
IEEE DOI 2211
Human intelligence, Dark matter, Benchmark testing, Iterative methods, Task analysis, Unsupervised learning, contrastive learning BibRef

Fu, M.H.[Ming-Hao], Cao, Y.H.[Yun-Hao], Wu, J.X.[Jian-Xin],
Worst Case Matters for Few-Shot Recognition,
ECCV22(XX:99-115).
Springer DOI 2211
BibRef

Yi, K.[Kai], Shen, X.Q.[Xiao-Qian], Gou, Y.H.[Yun-Hao], Elhoseiny, M.[Mohamed],
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification,
ECCV22(XX:116-132).
Springer DOI 2211
BibRef

Lai, J.X.[Jin-Xiang], Yang, S.[Siqian], Liu, W.L.[Wen-Long], Zeng, Y.[Yi], Huang, Z.Y.[Zhong-Yi], Wu, W.L.[Wen-Long], Liu, J.[Jun], Gao, B.B.[Bin-Bin], Wang, C.J.[Cheng-Jie],
tSF: Transformer-Based Semantic Filter for Few-Shot Learning,
ECCV22(XX:1-19).
Springer DOI 2211
BibRef

Hu, Y.[Yanxu], Ma, A.J.[Andy J.],
Adversarial Feature Augmentation for Cross-domain Few-Shot Classification,
ECCV22(XX:20-37).
Springer DOI 2211
BibRef

Ma, R.K.[Rong-Kai], Fang, P.F.[Peng-Fei], Avraham, G.[Gil], Zuo, Y.[Yan], Zhu, T.Y.[Tian-Yu], Drummond, T.[Tom], Harandi, M.[Mehrtash],
Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning,
ECCV22(XX:257-274).
Springer DOI 2211
BibRef

Comer, J.F.[Joseph F.], Jacobson, P.L.[Philip L.], Hoffmann, H.[Heiko],
Few-Shot Image Classification Along Sparse Graphs,
L3D-IVU22(4186-4194)
IEEE DOI 2210
Target tracking, Limiting, Shape, Training data, Streaming media, Pattern recognition, Reliability BibRef

Ye, M.[Meng], Lin, X.[Xiao], Burachas, G.[Giedrius], Divakaran, A.[Ajay], Yao, Y.[Yi],
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning,
ECV22(2725-2734)
IEEE DOI 2210
Training, Representation learning, Measurement, Interpolation, Prototypes, Inference algorithms, Pattern recognition BibRef

Liu, Y.[Yang], Zhang, W.F.[Wei-Feng], Xiang, C.[Chao], Zheng, T.[Tu], Cai, D.[Deng], He, X.F.[Xiao-Fei],
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification,
CVPR22(14391-14400)
IEEE DOI 2210
Atmospheric measurements, Markov processes, Particle measurements, Pattern recognition, Task analysis, Self- semi- meta- unsupervised learning BibRef

Lee, S.B.[Su-Been], Moon, W.J.[Won-Jun], Heo, J.P.[Jae-Pil],
Task Discrepancy Maximization for Fine-grained Few-Shot Classification,
CVPR22(5321-5330)
IEEE DOI 2210
Quadrature amplitude modulation, Focusing, Benchmark testing, Time division multiplexing, Encoding, Pattern recognition, Recognition: detection BibRef

Chikontwe, P.[Philip], Kim, S.[Soopil], Park, S.H.[Sang Hyun],
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification,
CVPR22(14534-14543)
IEEE DOI 2210
Training, Solid modeling, Head, Transfer learning, Prototypes, Feature extraction, Self- semi- meta- Recognition: detection, Representation learning BibRef

Ling, J.[Jie], Liao, L.[Lei], Yang, M.[Meng], Shuai, J.[Jia],
Semi-Supervised Few-shot Learning via Multi-Factor Clustering,
CVPR22(14544-14553)
IEEE DOI 2210
Manifolds, Learning systems, Codes, Fuses, Collaboration, Benchmark testing, Self- semi- meta- Recognition: detection, retrieval BibRef

Kang, D.[Dahyun], Cho, M.[Minsu],
Integrative Few-Shot Learning for Classification and Segmentation,
CVPR22(9969-9980)
IEEE DOI 2210
Image segmentation, Correlation, Computer network reliability, Semantics, Benchmark testing, Pattern recognition, Transfer/low-shot/long-tail learning BibRef

Xu, J.Y.[Jing-Yi], Le, H.[Hieu],
Generating Representative Samples for Few-Shot Classification,
CVPR22(8993-9003)
IEEE DOI 2210
Training, Visualization, Codes, Semantics, Pattern recognition, Transfer/low-shot/long-tail learning, Statistical methods BibRef

Hu, S.X.[Shell Xu], Li, D.[Da], Stühmer, J.[Jan], Kim, M.Y.[Min-Young], Hospedales, T.M.[Timothy M.],
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference,
CVPR22(9058-9067)
IEEE DOI 2210
Training, Pipelines, Transfer learning, Benchmark testing, Transformers, Self- semi- meta- unsupervised learning BibRef

Liang, K.J.[Kevin J.], Rangrej, S.B.[Samrudhdhi B.], Petrovic, V.[Vladan], Hassner, T.[Tal],
Few-shot Learning with Noisy Labels,
CVPR22(9079-9088)
IEEE DOI 2210
Training, Computational modeling, Prototypes, Transformers, Robustness, Pattern recognition, Transfer/low-shot/long-tail learning BibRef

Xie, J.T.[Jiang-Tao], Long, F.[Fei], Lv, J.M.[Jia-Ming], Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua],
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification,
CVPR22(7962-7971)
IEEE DOI 2210
Training, Deep learning, Transfer learning, Image representation, Extraterrestrial measurements, Pattern recognition, Statistical methods 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

Zhao, R.J.[Rui-Jing], Zhu, K.[Kai], Cao, Y.[Yang], Zha, Z.J.[Zheng-Jun],
AS-Net: Class-Aware Assistance and Suppression Network for Few-Shot Learning,
MMMod22(II:27-39).
Springer DOI 2203
BibRef

Li, S.[Suichan], Chen, D.D.[Dong-Dong], Chen, Y.P.[Yin-Peng], Yuan, L.[Lu], Zhang, L.[Lei], Chu, Q.[Qi], Liu, B.[Bin], Yu, N.H.[Neng-Hai],
Improve Unsupervised Pretraining for Few-label Transfer,
ICCV21(10181-10190)
IEEE DOI 2203
Annotations, Computational modeling, Clustering algorithms, Representation learning, Vision applications and systems BibRef

Ma, J.W.[Jia-Wei], Xie, H.C.[Han-Chen], Han, G.X.[Guang-Xing], Chang, S.F.[Shih-Fu], Galstyan, A.[Aram], Abd-Almageed, W.[Wael],
Partner-Assisted Learning for Few-Shot Image Classification,
ICCV21(10553-10562)
IEEE DOI 2203
Training, Learning systems, Visualization, Annotations, Prototypes, Benchmark testing, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Massiceti, D.[Daniela], Zintgraf, L.[Luisa], Bronskill, J.[John], Theodorou, L.[Lida], Harris, M.T.[Matthew Tobias], Cutrell, E.[Edward], Morrison, C.[Cecily], Hofmann, K.[Katja], Stumpf, S.[Simone],
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition,
ICCV21(10798-10808)
IEEE DOI 2203
Training, Technological innovation, Face recognition, Benchmark testing, Orbits, Robustness, Datasets and evaluation, Vision applications and systems BibRef

Baik, S.[Sungyong], Choi, J.[Janghoon], Kim, H.[Heewon], Cho, D.[Dohee], Min, J.[Jaesik], Lee, K.M.[Kyoung Mu],
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning,
ICCV21(9445-9454)
IEEE DOI 2203
Learning systems, Metals, Task analysis, Optimization, Transfer/Low-shot/Semi/Unsupervised Learning, Efficient training and inference methods BibRef

Li, W.H.[Wei-Hong], Liu, X.L.[Xia-Lei], Bilen, H.[Hakan],
Cross-domain Few-shot Learning with Task-specific Adapters,
CVPR22(7151-7160)
IEEE DOI 2210
BibRef
Earlier:
Universal Representation Learning from Multiple Domains for Few-shot Classification,
ICCV21(9506-9515)
IEEE DOI 2203
Training, Analytical models, Systematics, Costs, Computational modeling, Estimation, retrieval. Uniform resource locators, Representation learning, Knowledge engineering, Visualization, Computer aided instruction, Recognition and classification BibRef

Das, R.[Rajshekhar], Wang, Y.X.[Yu-Xiong], Moura, J.M.F.[José M. F.],
On the Importance of Distractors for Few-Shot Classification,
ICCV21(9010-9020)
IEEE DOI 2203
Training, Codes, Stochastic processes, Performance gain, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning 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

Phoo, C.P.[Cheng Perng], Hariharan, B.[Bharath],
Coarsely-labeled Data for Better Few-shot Transfer,
ICCV21(9032-9041)
IEEE DOI 2203
Representation learning, Codes, Filtering, Buildings, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning BibRef

Chen, Y.B.[Yin-Bo], Liu, Z.A.[Zhu-Ang], Xu, H.J.[Hui-Juan], Darrell, T.J.[Trevor J.], Wang, X.L.[Xiao-Long],
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning,
ICCV21(9042-9051)
IEEE DOI 2203
Training, Measurement, Codes, Classification algorithms, Task analysis, Standards, BibRef

Chowdhury, A.[Arkabandhu], Jiang, M.[Mingchao], Chaudhuri, S.[Swarat], Jermaine, C.[Chris],
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier,
ICCV21(9425-9434)
IEEE DOI 2203
Transfer learning, Feature extraction, Libraries, Computational efficiency, Classification algorithms, Feeds, Vision applications and systems BibRef

Zhang, C.[Chi], Ding, H.H.[Heng-Hui], Lin, G.S.[Guo-Sheng], Li, R.[Ruibo], Wang, C.H.[Chang-Hu], Shen, C.H.[Chun-Hua],
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning,
ICCV21(9415-9424)
IEEE DOI 2203
Adaptation models, Machine learning algorithms, Navigation, Machine learning, Benchmark testing, Classification algorithms, Recognition and classification BibRef

Lazarou, M.[Michalis], Stathaki, T.[Tania], Avrithis, Y.[Yannis],
Iterative label cleaning for transductive and semi-supervised few-shot learning,
ICCV21(8731-8740)
IEEE DOI 2203
Manifolds, Codes, Semisupervised learning, Prediction algorithms, Cleaning, Inference algorithms, Recognition and classification BibRef

Xu, J.Y.[Jing-Yi], Le, H.[Hieu], Huang, M.Z.[Ming-Zhen], Athar, S.[Shah_Rukh], Samaras, D.[Dimitris],
Variational Feature Disentangling for Fine-Grained Few-Shot Classification,
ICCV21(8792-8801)
IEEE DOI 2203
Codes, Lighting, Benchmark testing, Task analysis, Image classification, BibRef

Kang, D.[Dahyun], Kwon, H.[Heeseung], Min, J.[Juhong], Cho, M.[Minsu],
Relational Embedding for Few-Shot Classification,
ICCV21(8802-8813)
IEEE DOI 2203
Training, Visualization, Tensors, Image recognition, Correlation, Transforms, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning BibRef

Huang, K.[Kai], Geng, J.[Jie], Jiang, W.[Wen], Deng, X.Y.[Xin-Yang], Xu, Z.[Zhe],
Pseudo-loss Confidence Metric for Semi-supervised Few-shot Learning,
ICCV21(8651-8660)
IEEE DOI 2203
Measurement, Training, Weight measurement, Learning systems, Estimation, Multitasking, Extraterrestrial measurements, Recognition and classification BibRef

Yang, L.[Lihe], Zhuo, W.[Wei], Qi, L.[Lei], Shi, Y.H.[Ying-Huan], Gao, Y.[Yang],
Mining Latent Classes for Few-shot Segmentation,
ICCV21(8701-8710)
IEEE DOI 2203
Training, Costs, Codes, Training data, Prototypes, Benchmark testing, Transfer/Low-shot/Semi/Unsupervised Learning, Segmentation, grouping and shape BibRef

Fei, N.Y.[Nan-Yi], Gao, Y.Z.[Yi-Zhao], Lu, Z.W.[Zhi-Wu], Xiang, T.[Tao],
Z-Score Normalization, Hubness, and Few-Shot Learning,
ICCV21(142-151)
IEEE DOI 2203
Visualization, Prototypes, Benchmark testing, Boosting, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zhang, X.[Xueting], Meng, D.B.[De-Bin], Gouk, H.[Henry], Hospedales, T.[Timothy],
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition,
ICCV21(631-640)
IEEE DOI 2203
Training, Representation learning, Measurement, Uncertainty, Memory management, Feature extraction, Transfer/Low-shot/Semi/Unsupervised Learning 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

Zhou, Z.Q.[Zi-Qi], Qiu, X.[Xi], Xie, J.T.[Jiang-Tao], Wu, J.[Jianan], Zhang, C.[Chi],
Binocular Mutual Learning for Improving Few-shot Classification,
ICCV21(8382-8391)
IEEE DOI 2203
Learning systems, Degradation, Computational modeling, Decision making, Focusing, Performance gain, Recognition and classification BibRef

Qi, G.D.[Guo-Dong], Yu, H.M.[Hui-Min], Lu, Z.H.[Zhao-Hui], Li, S.Z.[Shu-Zhao],
Transductive Few-Shot Classification on the Oblique Manifold,
ICCV21(8392-8402)
IEEE DOI 2203
Manifolds, Measurement, Machine learning, Benchmark testing, Feature extraction, Approximation algorithms, Recognition and classification BibRef

Wu, J.[Jiamin], Zhang, T.Z.[Tian-Zhu], Zhang, Y.D.[Yong-Dong], Wu, F.[Feng],
Task-aware Part Mining Network for Few-Shot Learning,
ICCV21(8413-8422)
IEEE DOI 2203
Adaptation models, Computational modeling, Benchmark testing, Generators, Task analysis, Standards, Recognition and classification BibRef

Liu, Y.B.[Yan-Bin], Lee, J.H.[Ju-Ho], Zhu, L.C.[Lin-Chao], Chen, L.[Ling], Shi, H.[Humphrey], Yang, Y.[Yi],
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification,
ICCV21(8433-8442)
IEEE DOI 2203
Training, Extrapolation, Correlation, Computational modeling, Modulation, Information sharing, 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

Lazarou, M.[Michalis], Stathaki, T.[Tania], Avrithis, Y.[Yannis],
Tensor feature hallucination for few-shot learning,
WACV22(2050-2060)
IEEE DOI 2202
Training, Representation learning, Tensors, Focusing, Performance gain, Generative adversarial networks, GANs 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

Bateni, P.[Peyman], Barber, J.[Jarred], van de Meent, J.W.[Jan-Willem], Wood, F.[Frank],
Enhancing Few-Shot Image Classification with Unlabelled Examples,
WACV22(1597-1606)
IEEE DOI 2202
Training, Codes, Computational modeling, Benchmark testing, Feature extraction, Data mining, Transfer, 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

Simon, C.[Christian], Koniusz, P.[Piotr], Harandi, M.[Mehrtash],
Meta-Learning for Multi-Label Few-Shot Classification,
WACV22(346-355)
IEEE DOI 2202
Microwave integrated circuits, Protocols, Predictive models, Benchmark testing, Inference algorithms, Semi- and Un- supervised Learning Deep Learning BibRef

Yang, P.[Peng], Ren, S.G.[Shao-Gang], Zhao, Y.[Yang], Li, P.[Ping],
Calibrating CNNs for Few-Shot Meta Learning,
WACV22(408-417)
IEEE DOI 2202
Training, Adaptation models, Neuroscience, Neurons, Benchmark testing, Calibration, Transfer, Learning and Optimization BibRef

Zheng, P.X.[Pei-Xiao], Guo, X.[Xin], Qi, L.[Lin],
Edge-Labeling Based Directed Gated Graph Network for Few-Shot Learning,
ICIP21(544-548)
IEEE DOI 2201
Backpropagation, Convolution, Image processing, Image edge detection, Neural networks, Logic gates, CNN, GRU BibRef

Liang, Z.Y.[Zi-Yun], Gu, Y.[Yun], Yang, J.[Jie],
Hardmix: A Regularization Method to Mitigate the Large Shift in Few-Shot Domain Adaptation,
ICIP21(454-458)
IEEE DOI 2201
Training, Bridges, Image processing, Training data, Benchmark testing, Classification algorithms, Domain Adaptation, Mix-Up BibRef

Cheng, Y.C.[Yuan-Chia], Lin, C.S.[Ci-Siang], Yang, F.E.[Fu-En], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains,
ICIP21(434-438)
IEEE DOI 2201
Training, Visualization, Image recognition, Target recognition, Data models, Task analysis, few-shot learning BibRef

Liu, S.[Sihan], Wang, Y.[Yue],
Few-shot Learning with Online Self-Distillation,
VIPriors21(1067-1070)
IEEE DOI 2112
Training, Adaptation models, Pipelines, Benchmark testing, Data models BibRef

Stojanov, S.[Stefan], Thai, A.[Anh], Rehg, J.M.[James M.],
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias,
CVPR21(1798-1808)
IEEE DOI 2111
Shape, Psychology, Cognition, Pattern recognition, Object recognition BibRef

Hong, J.[Jie], Fang, P.F.[Peng-Fei], Li, W.H.[Wei-Hao], Zhang, T.[Tong], Simon, C.[Christian], Harandi, M.[Mehrtash], Petersson, L.[Lars],
Reinforced Attention for Few-Shot Learning and Beyond,
CVPR21(913-923)
IEEE DOI 2111
Image recognition, Computational modeling, Reinforcement learning, Prediction algorithms, Data models, Pattern recognition BibRef

Tang, S.X.[Shi-Xiang], Chen, D.P.[Da-Peng], Bai, L.[Lei], Liu, K.J.[Kai-Jian], Ge, Y.X.[Yi-Xiao], Ouyang, W.L.[Wan-Li],
Mutual CRF-GNN for Few-shot Learning,
CVPR21(2329-2339)
IEEE DOI 2111
Computational modeling, Semantics, Benchmark testing, Probabilistic logic, Market research, Pattern recognition BibRef

Zhang, B.Q.[Bao-Quan], Li, X.[Xutao], Ye, Y.M.[Yun-Ming], Huang, Z.C.[Zhi-Chao], Zhang, L.[Lisai],
Prototype Completion with Primitive Knowledge for Few-Shot Learning,
CVPR21(3753-3761)
IEEE DOI 2111
Knowledge engineering, Codes, Annotations, Computational modeling, Prototypes, Feature extraction BibRef

Chen, C.F.[Chao-Fan], Yang, X.S.[Xiao-Shan], Xu, C.S.[Chang-Sheng], Huang, X.[Xuhui], Ma, Z.[Zhe],
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning,
CVPR21(6592-6601)
IEEE DOI 2111
Visualization, Art, Computational modeling, Knowledge representation, Benchmark testing, Calibration BibRef

Wertheimer, D.[Davis], Tang, L.[Luming], Hariharan, B.[Bharath],
Few-Shot Classification with Feature Map Reconstruction Networks,
CVPR21(8008-8017)
IEEE DOI 2111
Computational modeling, Benchmark testing, Pattern recognition, Computational efficiency BibRef

Zhang, H.G.[Hong-Guang], Koniusz, P.[Piotr], Jian, S.[Songlei], Li, H.D.[Hong-Dong], Torr, P.H.S.[Philip H. S.],
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning,
CVPR21(9427-9436)
IEEE DOI 2111
Training, Learning systems, Protocols, Annotations, Animals, Semantics BibRef

Yue, X.Y.[Xiang-Yu], Zheng, Z.W.[Zang-Wei], Zhang, S.H.[Shang-Hang], Gao, Y.[Yang], Darrell, T.J.[Trevor J.], Keutzer, K.[Kurt], Vincentelli, A.S.[Alberto Sangiovanni],
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation,
CVPR21(13829-13839)
IEEE DOI 2111
Semantics, Predictive models, Benchmark testing, Pattern recognition BibRef

Chen, Z.Y.[Zheng-Yu], Ge, J.X.[Ji-Xie], Zhan, H.[Heshen], Huang, S.[Siteng], Wang, D.L.[Dong-Lin],
Pareto Self-Supervised Training for Few-Shot Learning,
CVPR21(13658-13667)
IEEE DOI 2111
Training, Pareto optimization, Benchmark testing, Space exploration, Pattern recognition, Task analysis BibRef

Rizve, M.N.[Mamshad Nayeem], Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Shah, M.[Mubarak],
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning,
CVPR21(10831-10841)
IEEE DOI 2111
Training, Measurement, Benchmark testing, Pattern recognition, Task analysis, Optimization BibRef

Zhao, Y.[Yang], Li, C.Y.[Chun-Yyan], Yu, P.[Ping], Chen, C.Y.[Chang-You],
ReMP: Rectified Metric Propagation for Few-Shot Learning,
LLID21(2581-2590)
IEEE DOI 2109
Training, Force, Prototypes, Performance gain, Extraterrestrial measurements BibRef

Chen, Z.T.[Zi-Tian], Maji, S.[Subhransu], Learned-Miller, E.G.[Erik G.],
Shot in the Dark: Few-Shot Learning with No Base-Class Labels,
LLID21(2662-2671)
IEEE DOI 2109
Supervised learning, Robustness, Pattern recognition 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

Mazumder, P.[Pratik], Singh, P.[Pravendra], Namboodiri, V.P.[Vinay P.],
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task,
WACV21(2653-2662)
IEEE DOI 2106
Learning systems, Training, Radio frequency, Neural networks, Benchmark testing BibRef

Mazumder, P.[Pratik], Singh, P.[Pravendra], Namboodiri, V.P.[Vinay P.],
RNNP: A Robust Few-Shot Learning Approach,
WACV21(2663-2672)
IEEE DOI 2106
Learning systems, Training, Prototypes, Noise measurement, Labeling BibRef

Azad, R.[Reza], Fayjie, A.R.[Abdur R.], Kauffmann, C.[Claude], Ben Ayed, I.[Ismail], Pedersoli, M.[Marco], Dolz, J.[Jose],
On the Texture Bias for Few-Shot CNN Segmentation,
WACV21(2673-2682)
IEEE DOI 2106
Training, Visualization, Image segmentation, Shape, Semantics, Prototypes, Bidirectional control BibRef

Liu, G.[Ge], Zhao, L.L.[Ling-Lan], Li, W.[Wei], Guo, D.[Dashan], Fang, X.Z.[Xiang-Zhong],
Class-wise Metric Scaling for Improved Few-Shot Classification,
WACV21(586-595)
IEEE DOI 2106
Measurement, Training, Refining, Performance gain, Feature extraction, Convex functions BibRef

Zhang, J.H.[Jian-Hong], Zhang, M.[Manli], Lu, Z.W.[Zhi-Wu], Xiang, T.[Tao],
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning,
WACV21(3481-3490)
IEEE DOI 2106
Training, Adaptation models, Noise reduction, Training data, Search problems, Data models BibRef

Zhang, G.J.[Gong-Jie], Cui, K.W.[Kai-Wen], Wu, R.L.[Rong-Liang], Lu, S.J.[Shi-Jian], Tian, Y.H.[Yong-Hong],
PNPDet: Efficient Few-shot Detection without Forgetting via Plug-and-Play Sub-networks,
WACV21(3822-3831)
IEEE DOI 2106
Measurement, Bridges, Detectors, Visual systems 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

Fortin, M.P.[Mathieu Pagé], Chaib-draa, B.[Brahim],
Towards Contextual Learning in Few-shot Object Classification,
WACV21(3278-3287)
IEEE DOI 2106
Visualization, Semantics, Genomics, Bioinformatics BibRef

Zhang, X.[Xu], Zhang, Y.[Youjia], Zhang, Z.[Zuyu],
Multi-granularity Recurrent Attention Graph Neural Network for Few-shot Learning,
MMMod21(II:147-158).
Springer DOI 2106
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Wang, H.J.[Hao-Jie], Lian, J.Y.[Jie-Ya], Xiong, S.W.[Sheng-Wu],
Few-shot Learning with Unlabeled Outlier Exposure,
MMMod21(I:340-351).
Springer DOI 2106
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Matsumi, S.[Susumu], Yamada, K.[Keiichi],
Few-Shot Learning Based on Metric Learning Using Class Augmentation,
ICPR21(196-201)
IEEE DOI 2105
Measurement, Support vector machines, Training data, Machine learning, Nearest neighbor methods, Extraterrestrial measurements BibRef

Wu, W.[Wei], Pang, S.[Shanmin], Tian, Z.Q.[Zhi-Qiang], Li, Y.[Yaochen],
Meta Generalized Network for Few-Shot Classification,
ICPR21(1400-1405)
IEEE DOI 2105
Training, Measurement, Adaptation models, Image recognition, Benchmark testing, Feature extraction, Pattern recognition BibRef

Lifchitz, Y.[Yann], Avrithis, Y.[Yannis], Picard, S.[Sylvaine],
Few-Shot Few-Shot Learning and the role of Spatial Attention,
ICPR21(2693-2700)
IEEE DOI 2105
Training, Focusing, Benchmark testing, Pattern recognition, Task analysis, Clutter, Standards BibRef

Nguyen, K.[Khoi], Todorovic, S.[Sinisa],
A Self-supervised GAN for Unsupervised Few-shot Object Recognition,
ICPR21(3225-3231)
IEEE DOI 2105
Training, Image coding, Performance gain, Probabilistic logic, Pattern recognition, Object recognition, Image reconstruction BibRef

Sun, J.[Jiamei], Lapuschkin, S.[Sebastian], Samek, W.[Wojciech], Zhao, Y.Q.[Yun-Qing], Cheung, N.M.[Ngai-Man], Binder, A.[Alexander],
Explanation-Guided Training for Cross-Domain Few-Shot Classification,
ICPR21(7609-7616)
IEEE DOI 2105
Training, Heating systems, Visualization, Computational modeling, Predictive models, Power capacitors, Pattern recognition 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

Hu, Y.Q.[Yu-Qing], Gripon, V.[Vincent], Pateux, S.[Stéphane],
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification,
ICPR21(8164-8171)
IEEE DOI 2105
Interpolation, Feature extraction, Graph neural networks, Pattern recognition, Standards, Logistics BibRef

Yan, B.M.[Bao-Ming], Zhou, C.[Chen], Zhao, B.[Bo], Guo, K.[Kan], Yang, J.[Jiang], Li, X.B.[Xiao-Bo], Zhang, M.[Ming], Wang, Y.Z.[Yi-Zhou],
Augmented Bi-path Network for Few-shot Learning,
ICPR21(8461-8468)
IEEE DOI 2105
Training, Visualization, Neural networks, Merging, Training data, Feature extraction, Robustness BibRef

Wang, Z.[Zhe], Liu, L.[Li], Li, F.[FanZhang],
TAAN: Task-Aware Attention Network for Few-shot Classification,
ICPR21(9130-9136)
IEEE DOI 2105
Training, Measurement, Transforms, Benchmark testing, Feature extraction, Pattern recognition, task-relevant channel attention BibRef

Lifchitz, Y.[Yann], Avrithis, Y.[Yannis], Picard, S.[Sylvaine],
Local Propagation for Few-Shot Learning,
ICPR21(10457-10464)
IEEE DOI 2105
Image representation, Pattern recognition, Standards BibRef

Cai, C.H.[Chun-Hao], Yuan, M.L.[Ming-Lei], Lu, T.[Tong],
IFSM: An Iterative Feature Selection Mechanism for Few-Shot Image Classification,
ICPR21(9429-9436)
IEEE DOI 2105
Learning systems, Training data, Network architecture, Jitter, Feature extraction, Reliability engineering, Pattern recognition, feature selection BibRef

Tseng, H.Y.[Hung-Yu], Chen, Y.W.[Yi-Wen], Tsai, Y.H.[Yi-Hsuan], Liu, S.[Sifei], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan],
Regularizing Meta-learning via Gradient Dropout,
ACCV20(IV:218-234).
Springer DOI 2103
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Perrett, T.[Toby], Masullo, A.[Alessandro], Burghardt, T.[Tilo], Mirmehdi, M.[Majid], Damen, D.[Dima],
Meta-learning with Context-Agnostic Initialisations,
ACCV20(IV:70-86).
Springer DOI 2103
For few-shot by finding initial result to fine-tune. 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
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Guan, J.[Jiechao], Zhang, M.[Manli], Lu, Z.W.[Zhi-Wu],
Large-scale Cross-domain Few-shot Learning,
ACCV20(III:474-491).
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Few-shot Image Recognition with Manifolds,
ISVC20(II:3-14).
Springer DOI 2103
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Liu, C.H.[Cheng-Hao], Wang, Z.H.[Zhi-Hao], Sahoo, D.[Doyen], Fang, Y.[Yuan], Zhang, K.[Kun], Hoi, S.C.H.[Steven C. H.],
Adaptive Task Sampling for Meta-learning,
ECCV20(XVIII:752-769).
Springer DOI 2012
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Guo, Y.H.[Yun-Hui], Codella, N.C.[Noel C.], Karlinsky, L.[Leonid], Codella, J.V.[James V.], Smith, J.R.[John R.], Saenko, K.[Kate], Rosing, T.[Tajana], Feris, R.[Rogerio],
A Broader Study of Cross-domain Few-shot Learning,
ECCV20(XXVII:124-141).
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Puri, R.[Rishi], Zakhor, A.[Avideh], Puri, R.[Raul],
Few Shot Learning For Point Cloud Data Using Model Agnostic Meta Learning,
ICIP20(1906-1910)
IEEE DOI 2011
Extend MAML. Task analysis, Feature extraction, Machine learning, Adaptation models, Neural networks, Training, 3D BibRef

Liu, X., Liu, P., Zong, L.,
Transductive Prototypical Network For Few-Shot Classification,
ICIP20(1671-1675)
IEEE DOI 2011
Prototypes, Training, Testing, Task analysis, Manganese, Neural networks, Semisupervised learning, Few-shot learning, transductive learning 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

Zhong, Q., Chen, L., Qian, Y.,
Few-Shot Learning for Remote Sensing Image Retrieval With MAML,
ICIP20(2446-2450)
IEEE DOI 2011
Image retrieval, Feature extraction, Training, Remote sensing, Task analysis, Data models, Histograms, Remote sensing, MAML BibRef

Rodríguez, P.[Pau], Laradji, I.[Issam], Drouin, A.[Alexandre], Lacoste, A.[Alexandre],
Embedding Propagation: Smoother Manifold for Few-shot Classification,
ECCV20(XXVI:121-138).
Springer DOI 2011
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Tian, Y.L.[Yong-Long], Wang, Y.[Yue], Krishnan, D.[Dilip], Tenenbaum, J.B.[Joshua B.], Isola, P.[Phillip],
Rethinking Few-shot Image Classification: A Good Embedding is All You Need?,
ECCV20(XIV:266-282).
Springer DOI 2011
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Su, J.C.[Jong-Chyi], Maji, S.[Subhransu], Hariharan, B.[Bharath],
When Does Self-supervision Improve Few-shot Learning?,
ECCV20(VII:645-666).
Springer DOI 2011
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Liu, Q.[Qing], Majumder, O.[Orchid], Achille, A.[Alessandro], Ravichandran, A.[Avinash], Bhotika, R.[Rahul], Soatto, S.[Stefano],
Incremental Few-shot Meta-learning via Indirect Discriminant Alignment,
ECCV20(VII:685-701).
Springer DOI 2011
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Lichtenstein, M.[Moshe], Sattigeri, P.[Prasanna], Feris, R.[Rogerio], Giryes, R.[Raja], Karlinsky, L.[Leonid],
Tafssl: Task-adaptive Feature Sub-space Learning for Few-shot Classification,
ECCV20(VII:522-539).
Springer DOI 2011
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Dvornik, N.[Nikita], Schmid, C.[Cordelia], Mairal, J.[Julien],
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification,
ECCV20(X:769-786).
Springer DOI 2011
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Wang, S.[Shuo], Yue, J.[Jun], Liu, J.Z.[Jian-Zhuang], Tian, Q.[Qi], Wang, M.[Meng],
Large-scale Few-shot Learning via Multi-modal Knowledge Discovery,
ECCV20(X:718-734).
Springer DOI 2011
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Kim, J.[Jaekyeom], Kim, H.[Hyoungseok], Kim, G.[Gunhee],
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning,
ECCV20(I:599-617).
Springer DOI 2011
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Nguyen, V.N.[Van Nhan], Løkse, S.[Sigurd], Wickstrøm, K.[Kristoffer], Kampffmeyer, M.[Michael], Roverso, D.[Davide], Jenssen, R.[Robert],
Sen: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-shot Learning Networks,
ECCV20(XXIII:118-134).
Springer DOI 2011
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Liu, J.[Jinlu], Song, L.[Liang], Qin, Y.Q.[Yong-Qiang],
Prototype Rectification for Few-shot Learning,
ECCV20(I:741-756).
Springer DOI 2011
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Liu, B.[Bin], Cao, Y.[Yue], Lin, Y.T.[Yu-Tong], Li, Q.[Qi], Zhang, Z.[Zheng], Long, M.S.[Ming-Sheng], Hu, H.[Han],
Negative Margin Matters: Understanding Margin in Few-Shot Classification,
ECCV20(IV:438-455).
Springer DOI 2011
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Afrasiyabi, A.[Arman], Lalonde, J.F.[Jean-François], Gagné, C.[Christian],
Associative Alignment for Few-shot Image Classification,
ECCV20(V:18-35).
Springer DOI 2011
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Monnier, T.[Tom], Vincent, E.[Elliot], Ponce, J.[Jean], Aubry, M.[Mathieu],
Unsupervised Layered Image Decomposition into Object Prototypes,
ICCV21(8620-8630)
IEEE DOI 2203
Social networking (online), Computational modeling, Prototypes, Predictive models, Benchmark testing, Image decomposition, Visual reasoning and logical representation 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
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Liu, Y.Y.[Yao-Yao], Schiele, B.[Bernt], Sun, Q.[Qianru],
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning,
ECCV20(XVI: 404-421).
Springer DOI 2010
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Guo, Y., Cheung, N.,
Attentive Weights Generation for Few Shot Learning via Information Maximization,
CVPR20(13496-13505)
IEEE DOI 2008
Task analysis, Feature extraction, Mutual information, Generators, Mathematical model, Adaptation models, Linear programming BibRef

Elsken, T., Staffler, B., Metzen, J.H., Hutter, F.,
Meta-Learning of Neural Architectures for Few-Shot Learning,
CVPR20(12362-12372)
IEEE DOI 2008
Task analysis, Training, Neural networks, Adaptation models, Standards, Machine learning BibRef

Li, A.[Aoxue], Huang, W.R.[Wei-Ran], Lan, X.[Xu], Feng, J.S.[Jia-Shi], Li, Z.G.[Zhen-Guo], Wang, L.W.[Li-Wei],
Boosting Few-Shot Learning With Adaptive Margin Loss,
CVPR20(12573-12581)
IEEE DOI 2008
Task analysis, Training, Semantics, Measurement, Additives, Mars, Generators BibRef

Yu, Z., Chen, L., Cheng, Z., Luo, J.,
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning,
CVPR20(12853-12861)
IEEE DOI 2008
Feature extraction, Training, Task analysis, Semisupervised learning, Data models, Entropy, Data mining BibRef

Yang, L., Li, L., Zhang, Z., Zhou, X., Zhou, E., Liu, Y.,
DPGN: Distribution Propagation Graph Network for Few-Shot Learning,
CVPR20(13387-13396)
IEEE DOI 2008
Pattern recognition BibRef

Tang, L., Wertheimer, D., Hariharan, B.,
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition,
CVPR20(14340-14349)
IEEE DOI 2008
Feature extraction, Training, Task analysis, Birds, Heating systems, Standards, Semantics BibRef

Bateni, P., Goyal, R., Masrani, V., Wood, F., Sigal, L.,
Improved Few-Shot Visual Classification,
CVPR20(14481-14490)
IEEE DOI 2008
Feature extraction, Task analysis, Euclidean distance, Prototypes, Computational modeling BibRef

Xue, Z., Xie, Z., Xing, Z., Duan, L.,
Relative Position and Map Networks in Few-shot Learning for Image Classification,
VL3W20(4032-4036)
IEEE DOI 2008
Measurement, Training, Feature extraction, Visualization, Task analysis, Neural networks, Computational modeling BibRef

Ye, H., Hu, H., Zhan, D., Sha, F.,
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions,
CVPR20(8805-8814)
IEEE DOI 2008
Task analysis, Visualization, Adaptation models, Feature extraction, Cats, Prototypes, Training BibRef

Zhou, L., Cui, P., Jia, X., Yang, S., Tian, Q.,
Learning to Select Base Classes for Few-Shot Classification,
CVPR20(4623-4632)
IEEE DOI 2008
Optimization, Testing, Data models, Training data, Adaptation models, Training, Bayes methods BibRef

Zhu, H.[Hao], Koniusz, P.[Piotr],
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning,
CVPR22(9068-9078)
IEEE DOI 2210
Learning systems, Codes, Benchmark testing, Data structures, Pattern recognition, Standards, Machine learning BibRef

Simon, C., Koniusz, P., Nock, R., Harandi, M.,
Adaptive Subspaces for Few-Shot Learning,
CVPR20(4135-4144)
IEEE DOI 2008
Prototypes, Task analysis, Feature extraction, Neural networks, Data models, Robustness, Machine learning BibRef

Fan, Q.[Qi], Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection with Model Calibration,
ECCV22(XIX:720-739).
Springer DOI 2211
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Fan, Q.[Qi], Zhuo, W., Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector,
CVPR20(4012-4021)
IEEE DOI 2008
Object detection, Training, Task analysis, Detectors, Proposals, Semantics BibRef

Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.,
Few-Shot Class-Incremental Learning,
CVPR20(12180-12189)
IEEE DOI 2008
Power capacitors, Training, Task analysis, Topology, Adaptation models, Neural networks, Network topology BibRef

Jena, R., Halder, S.S., Sycara, K.,
MA3: Model Agnostic Adversarial Augmentation for Few Shot learning,
VL3W20(3966-3970)
IEEE DOI 2008
Task analysis, Training, Transforms, Standards, Neural networks, Data models BibRef

Li, K., Zhang, Y., Li, K., Fu, Y.,
Adversarial Feature Hallucination Networks for Few-Shot Learning,
CVPR20(13467-13476)
IEEE DOI 2008
Generators, Task analysis, Data models, Training, Measurement, Neural networks BibRef

Rahimpour, A., Qi, H.,
Class-Discriminative Feature Embedding For Meta-Learning based Few-Shot Classification,
WACV20(3168-3176)
IEEE DOI 2006
Task analysis, Measurement, Training, Prototypes, Predictive models, Machine learning, Data models BibRef

Mangla, P., Singh, M., Sinha, A., Kumari, N., Balasubramanian, V.N., Krishnamurthy, B.,
Charting the Right Manifold: Manifold Mixup for Few-shot Learning,
WACV20(2207-2216)
IEEE DOI 2006
Task analysis, Manifolds, Training, Feature extraction, Robustness, Neural networks, Adaptation models BibRef

Chen, P.F.[Peng-Fei], Yuan, M.L.[Ming-Lei], Lu, T.[Tong],
Multi-scale Comparison Network for Few-shot Learning,
MMMod20(II:3-13).
Springer DOI 2003
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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
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Wang, X.[Xin], Yu, F.[Fisher], Wang, R.[Ruth], Darrell, T.J.[Trevor J.], Gonzalez, J.E.[Joseph E.],
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning,
CVPR19(1831-1840).
IEEE DOI 2002
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Zhang, H.G.[Hong-Guang], Zhang, J.[Jing], Koniusz, P.[Piotr],
Few-Shot Learning via Saliency-Guided Hallucination of Samples,
CVPR19(2765-2774).
IEEE DOI 2002
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Wu, Z.H.[Zhong-Hua], Shi, X.X.[Xiang-Xi], Lin, G.S.[Guo-Sheng], Cai, J.F.[Jian-Fei],
Learning Meta-class Memory for Few-Shot Semantic Segmentation,
ICCV21(497-506)
IEEE DOI 2203
Weight measurement, Training, Image quality, Image segmentation, Fuses, Semantics, Prototypes, Recognition and classification, Scene analysis and understanding BibRef

Zhang, C.[Chi], Lin, G.S.[Guo-Sheng], Liu, F.[Fayao], Yao, R.[Rui], Shen, C.H.[Chun-Hua],
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning,
CVPR19(5212-5221).
IEEE DOI 2002
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Chu, W.H.[Wen-Hsuan], Li, Y.J.[Yu-Jhe], Chang, J.C.[Jing-Cheng], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification,
CVPR19(6244-6253).
IEEE DOI 2002
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Alfassy, A.[Amit], Karlinsky, L.[Leonid], Aides, A.[Amit], Shtok, J.[Joseph], Harary, S.[Sivan], Feris, R.[Rogerio], Giryes, R.[Raja], Bronstein, A.M.[Alex M.],
LaSO: Label-Set Operations Networks for Multi-Label Few-Shot Learning,
CVPR19(6541-6550).
IEEE DOI 2002
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Wertheimer, D.[Davis], Hariharan, B.[Bharath],
Few-Shot Learning With Localization in Realistic Settings,
CVPR19(6551-6560).
IEEE DOI 2002
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Wang, T.[Tao], Zhang, X.P.[Xiao-Peng], Yuan, L.[Li], Feng, J.S.[Jia-Shi],
Few-Shot Adaptive Faster R-CNN,
CVPR19(7166-7175).
IEEE DOI 2002
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Fei, N.Y.[Nan-Yi], Guan, J.C.[Jie-Chao], Lu, Z.W.[Zhi-Wu], Gao, Y.Z.[Yi-Zhao],
Few-shot Zero-shot Learning: Knowledge Transfer with Less Supervision,
ACCV20(III:592-608).
Springer DOI 2103
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Li, A.[Aoxue], Luo, T.[Tiange], Lu, Z.W.[Zhi-Wu], Xiang, T.[Tao], Wang, L.W.[Li-Wei],
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy,
CVPR19(7205-7213).
IEEE DOI 2002
BibRef

Li, W.B.[Wen-Bin], Wang, L.[Lei], Xu, J.L.[Jing-Lin], Huo, J.[Jing], Gao, Y.[Yang], Luo, J.B.[Jie-Bo],
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning,
CVPR19(7253-7260).
IEEE DOI 2002
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Schonfeld, E.[Edgar], Ebrahimi, S.[Sayna], Sinha, S.[Samarth], Darrell, T.J.[Trevor J.], Akata, Z.[Zeynep],
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders,
CVPR19(8239-8247).
IEEE DOI 2002
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Dutta, A.[Anjan], Mancini, M.[Massimiliano], Akata, Z.[Zeynep],
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning,
DeepMTL21(2189-2198)
IEEE DOI 2112
Learning systems, Visualization, Protocols, Image recognition, Transfer learning, Semantics, Benchmark testing BibRef

Pastore, G.[Giuseppe], Cermelli, F.[Fabio], Xian, Y.Q.[Yong-Qin], Mancini, M.[Massimiliano], Akata, Z.[Zeynep], Caputo, B.[Barbara],
A Closer Look at Self-training for Zero-Label Semantic Segmentation,
LLID21(2687-2696)
IEEE DOI 2109
Training, Image segmentation, Semantics, Pipelines, Predictive models, Information filters, Pattern recognition BibRef

Xian, Y.Q.[Yong-Qin], Choudhury, S.[Subhabrata], He, Y.[Yang], Schiele, B.[Bernt], Akata, Z.[Zeynep],
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CVPR19(8248-8257).
IEEE DOI 2002
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Dense Classification and Implanting for Few-Shot Learning,
CVPR19(9250-9259).
IEEE DOI 2002
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Jamal, M.A.[Muhammad Abdullah], Qi, G.J.[Guo-Jun],
Task Agnostic Meta-Learning for Few-Shot Learning,
CVPR19(11711-11719).
IEEE DOI 2002
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Ye, M.[Meng], Guo, Y.H.[Yu-Hong],
Progressive Ensemble Networks for Zero-Shot Recognition,
CVPR19(11720-11728).
IEEE DOI 2002
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Atzmon, Y.[Yuval], Chechik, G.[Gal],
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning,
CVPR19(11663-11672).
IEEE DOI 2002
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Kampffmeyer, M.[Michael], Chen, Y.[Yinbo], Liang, X.D.[Xiao-Dan], Wang, H.[Hao], Zhang, Y.J.[Yu-Jia], Xing, E.P.[Eric P.],
Rethinking Knowledge Graph Propagation for Zero-Shot Learning,
CVPR19(11479-11488).
IEEE DOI 2002
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Tong, B.[Bin], Wang, C.[Chao], Klinkigt, M.[Martin], Kobayashi, Y.[Yoshiyuki], Nonaka, Y.[Yuuichi],
Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning,
CVPR19(11459-11468).
IEEE DOI 2002
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Hascoet, T.[Tristan], Ariki, Y.[Yasuo], Takiguchi, T.[Tetsuya],
On Zero-Shot Recognition of Generic Objects,
CVPR19(9545-9553).
IEEE DOI 2002
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Xie, G.S.[Guo-Sen], Liu, L.[Li], Jin, X.B.[Xiao-Bo], Zhu, F.[Fan], Zhang, Z.[Zheng], Qin, J.[Jie], Yao, Y.Z.[Ya-Zhou], Shao, L.[Ling],
Attentive Region Embedding Network for Zero-Shot Learning,
CVPR19(9376-9385).
IEEE DOI 2002
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Xie, G.S.[Guo-Sen], Liu, L.[Li], Zhu, F.[Fan], Zhao, F.[Fang], Zhang, Z.[Zheng], Yao, Y.Z.[Ya-Zhou], Qin, J.[Jie], Shao, L.[Ling],
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ECCV20(IV:562-580).
Springer DOI 2011
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Paul, A.[Akanksha], Krishnan, N.C.[Narayanan C.], Munjal, P.[Prateek],
Semantically Aligned Bias Reducing Zero Shot Learning,
CVPR19(7049-7058).
IEEE DOI 2002
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Ding, Z.M.[Zheng-Ming], Liu, H.F.[Hong-Fu],
Marginalized Latent Semantic Encoder for Zero-Shot Learning,
CVPR19(6184-6192).
IEEE DOI 2002
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Li, J.[Jin], Lan, X.G.[Xu-Guang], Liu, Y.[Yang], Wang, L.[Le], Zheng, N.N.[Nan-Ning],
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification,
CVPR19(5458-5467).
IEEE DOI 2002
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Zhu, P.K.[Peng-Kai], Wang, H.X.[Han-Xiao], Saligrama, V.[Venkatesh],
Generalized Zero-Shot Recognition Based on Visually Semantic Embedding,
CVPR19(2990-2998).
IEEE DOI 2002
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Pal, A.[Arghya], Balasubramanian, V.N.[Vineeth N.],
Zero-Shot Task Transfer,
CVPR19(2184-2193).
IEEE DOI 2002
BibRef

Sariyildiz, M.B.[Mert Bulent], Cinbis, R.G.[Ramazan Gokberk],
Gradient Matching Generative Networks for Zero-Shot Learning,
CVPR19(2163-2173).
IEEE DOI 2002
BibRef

Huang, H.[He], Wang, C.H.[Chang-Hu], Yu, P.S.[Philip S.], Wang, C.D.[Chang-Dong],
Generative Dual Adversarial Network for Generalized Zero-Shot Learning,
CVPR19(801-810).
IEEE DOI 2002
BibRef

Li, J.J.[Jing-Jing], Jing, M.M.[Meng-Meng], Lu, K.[Ke], Ding, Z.M.[Zheng-Ming], Zhu, L.[Lei], Huang, Z.[Zi],
Leveraging the Invariant Side of Generative Zero-Shot Learning,
CVPR19(7394-7403).
IEEE DOI 2002
BibRef

Li, H.Y.[Hong-Yang], Eigen, D.[David], Dodge, S.[Samuel], Zeiler, M.[Matthew], Wang, X.G.[Xiao-Gang],
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal,
CVPR19(1-10).
IEEE DOI 2002
BibRef

Kim, J.[Jongmin], Kim, T.[Taesup], Kim, S.[Sungwoong], Yoo, C.D.[Chang D.],
Edge-Labeling Graph Neural Network for Few-Shot Learning,
CVPR19(11-20).
IEEE DOI 2002
BibRef

Gidaris, S.[Spyros], Komodakis, N.[Nikos],
Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning,
CVPR19(21-30).
IEEE DOI 2002
BibRef

Sun, Q.R.[Qian-Ru], Liu, Y.Y.[Yao-Yao], Chua, T.S.[Tat-Seng], Schiele, B.[Bernt],
Meta-Transfer Learning for Few-Shot Learning,
CVPR19(403-412).
IEEE DOI 2002
BibRef

Pahde, F., Ostapenko, O., Hnichen, P.J., Klein, T., Nabi, M.,
Self-Paced Adversarial Training for Multimodal Few-Shot Learning,
WACV19(218-226)
IEEE DOI 1904
learning (artificial intelligence), neural nets, object recognition, Oxford-102 dataset, fine grained CUB dataset, Training data BibRef

Mehrotra, A., Dukkipati, A.,
Skip Residual Pairwise Networks With Learnable Comparative Functions for Few-Shot Learning,
WACV19(886-894)
IEEE DOI 1904
image representation, learning (artificial intelligence), mini-Imagenet dataset, skip residual pairwise networks, Data models BibRef

Pahde, F., Puscas, M., Wolff, J., Klein, T., Sebe, N., Nabi, M.,
Low-Shot Learning From Imaginary 3D Model,
WACV19(978-985)
IEEE DOI 1904
image classification, learning (artificial intelligence), neural nets, object recognition, set theory, Meta-Learning BibRef

Zhang, L.[Lu], Yang, X.[Xu], Liu, Z.Y.[Zhi-Yong], Qi, L.[Lu], Zhou, H.[Hao], Chiu, C.[Charles],
Single Shot Feature Aggregation Network for Underwater Object Detection,
ICPR18(1906-1911)
IEEE DOI 1812
Feature extraction, Object detection, Detectors, Task analysis, Training, Semantics, Convolutional neural networks BibRef

Xu, P., Zhao, X., Huang, K.,
Densely Connected Single-Shot Detector,
ICPR18(2178-2183)
IEEE DOI 1812
Feature extraction, Detectors, Object detection, Convolution, Transforms, Task analysis, Pattern recognition BibRef

Gidaris, S., Komodakis, N.,
Dynamic Few-Shot Visual Learning Without Forgetting,
CVPR18(4367-4375)
IEEE DOI 1812
Training, Feature extraction, Generators, Training data, Visualization, Object recognition, Task analysis BibRef

Qiao, S., Liu, C., Shen, W., Yuille, A.L.,
Few-Shot Image Recognition by Predicting Parameters from Activations,
CVPR18(7229-7238)
IEEE DOI 1812
Training, Neural networks, Visualization, Training data, Linearity BibRef

Wang, Y., Girshick, R., Hebert, M., Hariharan, B.,
Low-Shot Learning from Imaginary Data,
CVPR18(7278-7286)
IEEE DOI 1812
Training, Strain, Visualization, Data visualization, Task analysis, Feature extraction, Machine vision BibRef

Zhao, F.[Fang], Zhao, J.[Jian], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Dynamic Conditional Networks for Few-Shot Learning,
ECCV18(XV: 20-36).
Springer DOI 1810
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

Pahde, F.[Frederik], Nabi, M.[Main], Klein, T.[Tassila], Jahnichen, P.[Patrick],
Discriminative Hallucination for Multi-Modal Few-Shot Learning,
ICIP18(156-160)
IEEE DOI 1809
Training, Visualization, Birds, Machine learning, Training data, Task analysis, Few-Shot Learning, Multi-Modal, Fine-grained Recognition BibRef

Qi, H., Brown, M., Lowe, D.G.,
Low-Shot Learning with Imprinted Weights,
CVPR18(5822-5830)
IEEE DOI 1812
Training, Neural networks, Semantics, Google, Training data, Euclidean distance BibRef

Hariharan, B.[Bharath], Girshick, R.[Ross],
Low-Shot Visual Recognition by Shrinking and Hallucinating Features,
ICCV17(3037-3046)
IEEE DOI 1802
Recognize categories from very few examples. image recognition, learning (artificial intelligence), object recognition, feature hallucination, feature shrinking, Visualization BibRef

Xu, Z., Zhu, L., Yang, Y.,
Few-Shot Object Recognition from Machine-Labeled Web Images,
CVPR17(5358-5366)
IEEE DOI 1711
Google, Neural networks, Object recognition, Training, Visualization BibRef

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