14.1.9 Few Shot Learning

Chapter Contents (Back)
Small Sample Size. Few-Shot Learning. A subset:
See also Deep Few Shot Learning.
See also One Shot Learning.
See also Fine Tuning, Fine-Tuning, Pre-Training, Zero-Shot, One-Shot.

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

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

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

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

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

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

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

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, 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

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

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

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

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

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

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

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.M.[Zong-Min],
Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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

Schwartz, E.[Eli], Karlinsky, L.[Leonid], Feris, R.S.[Rogerio S.], 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

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

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

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

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

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

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

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

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

Tan, Q.[Qi], Wu, Z.Z.[Zong-Ze], Lai, J.L.[Jia-Lun], 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

Zha, Z.[Zican], Tang, H.[Hao], Sun, Y.L.[Yun-Lian], 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

Zhang, B.Q.[Bao-Quan], Li, X.[Xutao], Ye, Y.M.[Yun-Ming], Feng, S.S.[Shan-Shan],
Prototype Completion for Few-Shot Learning,
PAMI(45), No. 10, October 2023, pp. 12250-12268.
IEEE DOI 2310
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

Fan, C.Y.[Chen-You], Hu, J.J.[Jun-Jie], Huang, J.W.[Jian-Wei],
Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning,
PAMI(45), No. 10, October 2023, pp. 11810-11823.
IEEE DOI 2310
BibRef

Xu, R.J.[Ren-Jie], Xing, L.[Lei], Liu, B.[Baodi], Tao, D.P.[Da-Peng], Cao, W.J.[Wei-Jia], Liu, W.F.[Wei-Feng],
Cross-Domain Few-Shot classification via class-shared and class-specific dictionaries,
PR(144), 2023, pp. 109811.
Elsevier DOI 2310
Few-shot learning, Dictionary learning, Cross-Domain, Collaborative representation BibRef

Walsh, R.[Reece], Osman, I.[Islam], Shehata, M.S.[Mohamed S.],
Masked Embedding Modeling With Rapid Domain Adjustment for Few-Shot Image Classification,
IP(32), 2023, pp. 4907-4920.
IEEE DOI Code:
WWW Link. 2310
BibRef

Zhou, Z.Y.[Zhen-Yu], Luo, L.[Lei], Liao, Q.[Qing], Liu, X.W.[Xin-Wang], Zhu, E.[En],
Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints,
IP(32), 2023, pp. 5197-5208.
IEEE DOI 2310
BibRef

Qi, G.D.[Guo-Dong], Long, Y.Q.[Yang-Qi], Lu, Z.H.[Zhao-Hui], Yu, H.M.[Hui-Min],
Causal Intervention for Few-Shot Hypothesis Adaptation,
SPLetters(30), 2023, pp. 1267-1271.
IEEE DOI 2310
BibRef

Xu, R.J.[Ren-Jie], Yang, X.H.[Xing-Hao], Yao, X.X.[Xing-Xing], Tao, D.P.[Da-Peng], Cao, W.J.[Wei-Jia], Lu, X.P.[Xiao-Ping], Liu, W.F.[Wei-Feng],
Self-Paced Hard Task-Example Mining for Few-Shot Classification,
CirSysVideo(33), No. 10, October 2023, pp. 5631-5644.
IEEE DOI 2310
BibRef

Wu, S.N.[Si-Ning], Gao, X.[Xiang], Hu, X.P.[Xiao-Peng],
Task-Oriented Feature Hallucination for Few-Shot Image Classification,
IET-IPR(17), No. 12, 2023, pp. 3564-3579.
DOI Link 2310
image classification, image recognition, image representation, pattern recognition, supervised learning BibRef

Deng, S.[Shule], Yu, J.G.[Jin-Gang], Wu, Z.H.[Zi-Hao], Gao, H.X.[Hong-Xia], Li, Y.S.[Yan-Sheng], Yang, Y.[Yang],
Learning Relative Feature Displacement for Few-Shot Open-Set Recognition,
MultMed(25), 2023, pp. 5763-5774.
IEEE DOI 2311
BibRef

Liu, F.[Fan], Yang, S.[Sai], Chen, D.[Delong], Huang, H.X.[Hua-Xi], Zhou, J.[Jun],
Few-shot classification guided by generalization error bound,
PR(145), 2024, pp. 109904.
Elsevier DOI 2311
Few-shot classification, Generalization error bound, Self-supervised learning, Knowledge distillation BibRef

Shu, Y.[Yang], Cao, Z.J.[Zhang-Jie], Gao, J.H.[Jing-Han], Wang, J.M.[Jian-Min], Yu, P.S.[Philip S.], Long, M.S.[Ming-Sheng],
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning,
PAMI(45), No. 12, December 2023, pp. 15275-15291.
IEEE DOI 2311
BibRef

Li, W.B.[Wen-Bin], Wang, Z.[Ziyi], Yang, X.S.[Xue-Song], Dong, C.[Chuanqi], Tian, P.[Pinzhuo], Qin, T.[Tiexin], Huo, J.[Jing], Shi, Y.[Yinghuan], Wang, L.[Lei], Gao, Y.[Yang], Luo, J.B.[Jie-Bo],
LibFewShot: A Comprehensive Library for Few-Shot Learning,
PAMI(45), No. 12, December 2023, pp. 14938-14955.
IEEE DOI 2311
BibRef

Tian, P.Z.[Pin-Zhuo], Xie, S.R.[Shao-Rong],
An Adversarial Meta-Training Framework for Cross-Domain Few-Shot Learning,
MultMed(25), 2023, pp. 6881-6891.
IEEE DOI 2311
BibRef

Clay, V.[Viviane], Pipa, G.[Gordon], Kühnberger, K.U.[Kai-Uwe], König, P.[Peter],
Development of Few-Shot Learning Capabilities in Artificial Neural Networks When Learning Through Self-Supervised Interaction,
PAMI(46), No. 1, January 2024, pp. 209-219.
IEEE DOI 2312
BibRef

Park, S.[Sangwoo], Cohen, K.M.[Kfir M.], Simeone, O.[Osvaldo],
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction,
PAMI(46), No. 1, January 2024, pp. 280-291.
IEEE DOI 2312
BibRef

Sun, J.X.[Jia-Xing], Shen, X.B.[Xiao-Bo], Sun, Q.S.[Quan-Sen],
Efficient Feature Reconstruction via l2,1-Norm Regularization for Few-Shot Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7452-7465.
IEEE DOI 2312
BibRef

Wang, X.X.[Xi-Xi], Wang, X.[Xiao], Jiang, B.[Bo], Luo, B.[Bin],
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7789-7802.
IEEE DOI 2312
BibRef

Hamzaoui, M.[Manal], Chapel, L.[Laetitia], Pham, M.T.[Minh-Tan], Lefèvre, S.[Sébastien],
Hyperbolic prototypical network for few shot remote sensing scene classification,
PRL(177), 2024, pp. 151-156.
Elsevier DOI 2401
Few-shot learning, Hyperbolic space, Scene classification, Remote sensing BibRef

Li, Q.[Qiaonan], Wen, G.H.[Gui-Hua], Yang, P.[Pei],
From patch, sample to domain: Capture geometric structures for few-shot learning,
PR(148), 2024, pp. 110147.
Elsevier DOI 2402
Cross-domain, Few-shot learning, Optimal transport BibRef

Gao, R.X.[Rui-Xuan], Su, H.[Han], Prasad, S.[Shitala], Tang, P.[Peisen],
Few-shot classification with multisemantic information fusion network,
IVC(141), 2024, pp. 104869.
Elsevier DOI 2402
Few-shot learning, Metric-based learning, Feature representation, Unsupervised mechanism BibRef

Ye, H.J.[Han-Jia], Ming, L.[Lu], Zhan, D.C.[De-Chuan], Chao, W.L.[Wei-Lun],
Few-Shot Learning With a Strong Teacher,
PAMI(46), No. 3, March 2024, pp. 1425-1440.
IEEE DOI 2402
Task analysis, Training, Feature extraction, Benchmark testing, Visualization, Standards, Loss measurement, Few-shot learning, knowledge distillation BibRef

Feng, R.[Rui], Ji, H.B.[Hong-Bing], Zhu, Z.G.[Zhi-Gang], Wang, L.[Lei],
Global Information Embedding Network for Few-Shot Learning,
SPLetters(31), 2024, pp. 501-505.
IEEE DOI 2402
Feature extraction, Task analysis, Prototypes, Frequency-domain analysis, Training, Benchmark testing, contrastive learning BibRef

Shao, S.[Shuai], Wang, Y.[Yan], Liu, B.[Bin], Liu, W.F.[Wei-Feng], Wang, Y.J.[Yag-Jiang], Liu, B.[Baodi],
FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot Classification,
CirSysVideo(34), No. 2, February 2024, pp. 839-851.
IEEE DOI 2402
Data augmentation, Power capacitors, Frequency-domain analysis, Discrete Fourier transforms, Task analysis, Semantics, Data models, Fourier-augmentation based data-shunting BibRef

Han, M.Y.[Meng-Ya], Zhan, Y.B.[Yi-Bing], Luo, Y.[Yong], Hu, H.[Han], Su, K.[Kehua], Du, B.[Bo],
Textual Enhanced Adaptive Meta-Fusion for Few-Shot Visual Recognition,
MultMed(26), 2024, pp. 2408-2418.
IEEE DOI 2402
Visualization, Semantics, Task analysis, Metalearning, Training, Standards, Feature extraction, Few-shot visual recognition, multimodal fusion BibRef

Dong, Z.[Zhong], Lin, B.[Baojun], Xie, F.[Fang],
Optimizing Few-Shot Remote Sensing Scene Classification Based on an Improved Data Augmentation Approach,
RS(16), No. 3, 2024, pp. 525.
DOI Link 2402
BibRef

Wei, X.S.[Xiu-Shen], Xu, H.Y.[He-Yang], Yang, Z.W.[Zhi-Wen], Duan, C.L.[Chen-Long], Peng, Y.X.[Yu-Xin],
Negatives Make a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning,
PAMI(46), No. 4, April 2024, pp. 2091-2103.
IEEE DOI 2403
Multiple signal classification, Task analysis, Training, Predictive models, Data models, Computational modeling, Robustness, few-shot learning BibRef

Zheng, P.X.[Pei-Xiao], Guo, X.[Xin], Chen, E.[Enqing], Qi, L.[Lin], Guan, L.[Ling],
Edge-labeling based modified gated graph network for few-shot learning,
PR(150), 2024, pp. 110264.
Elsevier DOI Code:
WWW Link. 2403
BibRef
Earlier: A1, A2, A4, Only:
Edge-Labeling Based Directed Gated Graph Network for Few-Shot Learning,
ICIP21(544-548)
IEEE DOI 2201
Graph network, Few-shot learning, Gated recurrent unit, Edge-labeling. Backpropagation, Convolution, Image edge detection, Neural networks, Logic gates, CNN, GRU BibRef

Liu, W.D.[Wei-De], Wu, Z.H.[Zhong-Hua], Zhao, Y.[Yang], Fang, Y.M.[Yu-Ming], Foo, C.S.[Chuan-Sheng], Cheng, J.[Jun], Lin, G.S.[Guo-Sheng],
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation,
IJCV(132), No. 4, April 2024, pp. 1277-1291.
Springer DOI 2404
BibRef

Wu, W.X.[Wen-Xiao], Shao, Y.J.[Yuan-Jie], Gao, C.X.[Chang-Xin], Xue, J.H.[Jing-Hao], Sang, N.[Nong],
Query-centric distance modulator for few-shot classification,
PR(151), 2024, pp. 110380.
Elsevier DOI Code:
WWW Link. 2404
Few-shot classification, Distance metric learning-based, Channel-weighting, Query-centric distance modulator BibRef

Sun, L.Y.[Liang-Yu], Chu, W.T.[Wei-Ta],
Overall positive prototype for few-shot open-set recognition,
PR(151), 2024, pp. 110400.
Elsevier DOI 2404
Few-shot learning, Open-set recognition, Prototype BibRef

Ji, F.F.[Fan-Fan], Yuan, X.T.[Xiao-Tong], Liu, Q.S.[Qing-Shan],
Soft Weight Pruning for Cross-Domain Few-Shot Learning With Unlabeled Target Data,
MultMed(26), 2024, pp. 6759-6769.
IEEE DOI 2404
Feature extraction, Task analysis, Self-supervised learning, Training, Data models, Data mining, Deep learning, soft weight pruning BibRef

Xu, T.[Tuo], Wang, Y.[Ying], Li, J.[Jie], Du, Y.F.[Yue-Fan],
Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images,
RS(16), No. 7, 2024, pp. 1285.
DOI Link 2404
BibRef

Pan, M.H.[Mei-Hong], Shen, H.B.[Hong-Bin],
Cross-modal de-deviation for enhancing few-shot classification,
PR(152), 2024, pp. 110475.
Elsevier DOI Code:
WWW Link. 2405
Cross-modal label assignment, Alternating least squares, Alternative optimization, Closed-form solution BibRef

Pan, S.[Siduo], Zhang, Z.Q.[Zi-Qi], Wei, K.[Kun], Yang, X.[Xu], Deng, C.[Cheng],
Few-Shot Generative Model Adaptation via Style-Guided Prompt,
MultMed(26), 2024, pp. 7661-7672.
IEEE DOI 2405
Adaptation models, Training, Data models, Generators, Generative adversarial networks, Task analysis, Visualization, prompt learning BibRef

Lulu, Q.[Qi], Ranhui, X.[Xu], Shao-Jie, Z.[Zhao], Mingming, Z.[Zheng], Wei-Qin, Y.[Yu],
TMNIO: Triplet merged network with involution operators for improved few-shot image classification,
IET-IPR(18), No. 6, 2024, pp. 1629-1641.
DOI Link 2405
image classification, few-shot learning, contrastive learning, prototypical networks BibRef

Zhang, J.[Jinhu], Li, S.B.[Shao-Bo], Zhang, X.X.[Xing-Xing], Huang, Z.C.[Zi-Chen], Miao, H.[Hui],
Transductive semantic decoupling double variational inference for few-shot classification,
IVC(146), 2024, pp. 105034.
Elsevier DOI Code:
WWW Link. 2405
Few-shot, Variational inference, meta-learning, Latent embedding BibRef

Dang, Z.H.[Zhuo-Hang], Luo, M.[Minnan], Wang, J.H.[Ji-Hong], Jia, C.Y.[Cheng-You], Yan, C.X.[Cai-Xia], Dai, G.[Guang], Chang, X.J.[Xiao-Jun], Zheng, Q.H.[Qing-Hua],
Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning,
IP(33), 2024, pp. 3520-3535.
IEEE DOI Code:
WWW Link. 2406
Training, Feature extraction, Optimization, Mutual information, Task analysis, Generators, Semantics, Few-shot learning, information bottleneck BibRef


Zhang, J.J.[Jun-Jie], Rao, Y.[Yutao], Huang, X.S.[Xiao-Shui], Li, G.[Guanyi], Zhou, X.[Xin], Zeng, D.[Dan],
Frequency-Aware Multi-Modal Fine-Tuning for Few-Shot Open-Set Remote Sensing Scene Classification,
MultMed(26), 2024, pp. 7823-7837.
IEEE DOI 2405
Task analysis, Prototypes, Training, Visualization, Scene classification, Adaptation models, Semantics, parameter-efficient transfer learning BibRef

Moreira, G.[Gabriel], Marques, M.[Manuel], Costeira, J.P.[João Paulo], Hauptmann, A.[Alexander],
Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin,
WACV24(2071-2079)
IEEE DOI 2404
Representation learning, Geometry, Image recognition, Neural networks, Euclidean distance, Benchmark testing, Algorithms, Image recognition and understanding BibRef

Lee, G.Y.[Gao Yu], Dam, T.[Tanmoy], Poenar, D.P.[Daniel Puiu], Duong, V.N.[Vu N.], Ferdaus, M.M.[Md Meftahul],
HELA-VFA: A Hellinger Distance-Attention-based Feature Aggregation Network for Few-Shot Classification,
WACV24(2162-2172)
IEEE DOI 2404
Measurement, Current measurement, Aggregates, Benchmark testing, Feature extraction, Probabilistic logic, Algorithms, Image recognition and understanding BibRef

Feng, R.[Rui], Ji, H.B.[Hong-Bing], Zhu, Z.G.[Zhi-Gang], Wang, L.[Lei],
Wavelet Attention Network for Few-shot learning,
CVIDL23(484-488)
IEEE DOI 2403
Wide area networks, Wavelet transforms, Training, Representation learning, Interference, Benchmark testing, Attention mechanism BibRef

Xia, H.F.[Hai-Feng], Li, K.[Kai], Min, M.R.Q.[Martin Ren-Qiang], Ding, Z.M.[Zheng-Ming],
Few-Shot Video Classification via Representation Fusion and Promotion Learning,
ICCV23(19254-19263)
IEEE DOI 2401
BibRef

Yi, X.Y.[Xuan-Yu], Deng, J.J.[Jia-Jun], Sun, Q.[Qianru], Hua, X.S.[Xian-Sheng], Lim, J.H.[Joo-Hwee], Zhang, H.W.[Han-Wang],
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition,
ICCV23(14417-14428)
IEEE DOI 2401
BibRef

Hao, F.S.[Fu-Sheng], He, F.X.[Feng-Xiang], Liu, L.[Liu], Wu, F.X.[Fu-Xiang], Tao, D.C.[Da-Cheng], Cheng, J.[Jun],
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification,
ICCV23(18859-18869)
IEEE DOI Code:
WWW Link. 2401
BibRef

Hu, T.[Teng], Zhang, J.N.[Jiang-Ning], Liu, L.[Liang], Yi, R.[Ran], Kou, S.Q.[Si-Qi], Zhu, H.[Haokun], Chen, X.[Xu], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie], Ma, L.Z.[Li-Zhuang],
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption,
ICCV23(2406-2415)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lazarou, M.[Michalis], Avrithis, Y.[Yannis], Stathaki, T.[Tania],
Adaptive manifold for imbalanced transductive few-shot learning,
WACV24(2286-295)
IEEE DOI Code:
WWW Link. 2404
Manifolds, Source coding, Euclidean distance, Benchmark testing, Prediction algorithms, Inference algorithms, Loss measurement, Image recognition and understanding BibRef

Lazarou, M.[Michalis], Avrithis, Y.[Yannis], Ren, G.Y.[Guang-Yu], Stathaki, T.[Tania],
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning,
ICIP23(331-335)
IEEE DOI Code:
WWW Link. 2312
BibRef

Zhu, J.J.[Jun-Jie], Yang, K.[Ke], Qiu, C.P.[Chun-Ping], Dai, M.Y.[Meng-Yuan], Guan, N.Y.[Nai-Yang], Yi, X.D.[Xiao-Dong],
Hybrid Contrastive Prototypical Network for Few-Shot Scene Classification,
ICIP23(3588-3592)
IEEE DOI 2312
BibRef

Sun, L.[Li], Wang, L.[Liuan], Sun, J.[Jun], Okatani, T.[Takayuki],
Prompt Prototype Learning Based on Ranking Instruction For Few-Shot Visual Tasks,
ICIP23(3235-3239)
IEEE DOI 2312
BibRef

Xu, H.[Huali], Zhi, S.F.[Shuai-Feng], Liu, L.[Li],
Cross-Domain Few-Shot Classification Via Inter-Source Stylization,
ICIP23(565-569)
IEEE DOI 2312
BibRef

Trosten, D.J.[Daniel J.], Chakraborty, R.[Rwiddhi], Løksc, S.[Sigurd], Wickstrøm, K.K.[Kristoffer Knutsen], Jenssen, R.[Robert], Kampffmeyer, M.C.[Michael C.],
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings,
CVPR23(7527-7536)
IEEE DOI 2309
BibRef

Chen, W.T.[Wen-Tao], Si, C.Y.[Chen-Yang], Zhang, Z.[Zhang], Wang, L.[Liang], Wang, Z.[Zilei], Tan, T.N.[Tie-Niu],
Semantic Prompt for Few-Shot Image Recognition,
CVPR23(23581-23591)
IEEE DOI 2309
BibRef

Zhu, H.[Hao], Koniusz, P.[Piotr],
Transductive Few-Shot Learning with Prototype-Based Label Propagation by Iterative Graph Refinement,
CVPR23(23996-24006)
IEEE DOI 2309
BibRef

Boudiaf, M.[Malik], Bennequin, E.[Etienne], Tami, M.[Myriam], Toubhans, A.[Antoine], Piantanida, P.[Pablo], Hudelot, C.[Celine], Ayed, I.B.[Ismail Ben],
Open-Set Likelihood Maximization for Few-Shot Learning,
CVPR23(24007-24016)
IEEE DOI 2309
BibRef

Anvekar, T.[Tejas], Bazazian, D.[Dena],
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning,
DLGC23(4179-4188)
IEEE DOI 2309
BibRef

Parmar, V.[Vivek], Kingra, S.K.[Sandeep Kaur], Sarwar, S.S.[Syed Shakib], Li, Z.[Ziyun], de Salvo, B.[Barbara], Suri, M.[Manan],
Fully-Binarized Distance Computation based On-device Few-Shot Learning for XR applications,
EVW23(4502-4508)
IEEE DOI 2309
BibRef

Padmanabhan, D.C.[Deepan Chakravarthi], Gowda, S.[Shruthi], Arani, E.[Elahe], Zonooz, B.[Bahram],
LSFSL: Leveraging Shape Information in Few-shot Learning,
L3D-IVU23(4971-4980)
IEEE DOI 2309
BibRef

Wang, R.Q.[Run-Qi], Zheng, H.[Hao], Duan, X.Y.[Xiao-Yue], Liu, J.Z.[Jian-Zhuang], Lu, Y.N.[Yu-Ning], Wang, T.[Tian], Xu, S.[Songcen], Zhang, B.C.[Bao-Chang],
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment,
CVPR23(23445-23454)
IEEE DOI 2309
BibRef

Zhou, F.[Fei], Wang, P.[Peng], Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Revisiting Prototypical Network for Cross Domain Few-Shot Learning,
CVPR23(20061-20070)
IEEE DOI 2309
BibRef

Ma, T.Y.[Tian-Yi], Sun, Y.F.[Yi-Fan], Yang, Z.X.[Zong-Xin], Yang, Y.[Yi],
ProD: Prompting-to-disentangle Domain Knowledge for Cross-domain Few-shot Image Classification,
CVPR23(19754-19763)
IEEE DOI 2309
BibRef

Zhang, R.[Renrui], Hu, X.F.[Xiang-Fei], Li, B.[Bohao], Huang, S.Y.[Si-Yuan], Deng, H.Q.[Han-Qiu], Qiao, Y.[Yu], Gao, P.[Peng], Li, H.S.[Hong-Sheng],
Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners,
CVPR23(15211-15222)
IEEE DOI 2309
BibRef

Zhang, H.G.[Hong-Guang], Torr, P.H.S.[Philip H. S.], Koniusz, P.[Piotr],
Improving Few-shot Learning by Spatially-aware Matching and Crosstransformer,
ACCV22(V:3-20).
Springer DOI 2307
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

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

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

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

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

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

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

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.T.[Xue-Ting], Meng, D.B.[De-Bin], Gouk, H.[Henry], Hospedales, T.M.[Timothy M.],
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

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

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

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

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

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

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

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

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

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

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
BibRef

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
BibRef

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

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

Guan, J.[Jiechao], Zhang, M.[Manli], Lu, Z.W.[Zhi-Wu],
Large-scale Cross-domain Few-shot Learning,
ACCV20(III:474-491).
Springer DOI 2103
BibRef

Das, D.[Debasmit], Moon, J.H., Lee, C.S.G.[C. S. George],
Few-shot Image Recognition with Manifolds,
ISVC20(II:3-14).
Springer DOI 2103
BibRef

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.S.[Rogerio S.],
A Broader Study of Cross-domain Few-shot Learning,
ECCV20(XXVII:124-141).
Springer DOI 2011
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

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
BibRef

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
BibRef

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
BibRef

Lichtenstein, M.[Moshe], Sattigeri, P.[Prasanna], Feris, R.S.[Rogerio S.], Giryes, R.[Raja], Karlinsky, L.[Leonid],
Tafssl: Task-adaptive Feature Sub-space Learning for Few-shot Classification,
ECCV20(VII:522-539).
Springer DOI 2011
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Liu, J.L.[Jin-Lu], Song, L.[Liang], Qin, Y.Q.[Yong-Qiang],
Prototype Rectification for Few-shot Learning,
ECCV20(I:741-756).
Springer DOI 2011
BibRef

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
BibRef

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
BibRef

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

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
BibRef

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

Li, A.X.[Ao-Xue], 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

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

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Alfassy, A.[Amit], Karlinsky, L.[Leonid], Aides, A.[Amit], Shtok, J.[Joseph], Harary, S.[Sivan], Feris, R.S.[Rogerio S.], 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
BibRef

Wertheimer, D.[Davis], Hariharan, B.[Bharath],
Few-Shot Learning With Localization in Realistic Settings,
CVPR19(6551-6560).
IEEE DOI 2002
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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],
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation,
CVPR19(8248-8257).
IEEE DOI 2002
BibRef

Lifchitz, Y.[Yann], Avrithis, Y.[Yannis], Picard, S.[Sylvaine], Bursuc, A.[Andrei],
Dense Classification and Implanting for Few-Shot Learning,
CVPR19(9250-9259).
IEEE DOI 2002
BibRef

Ye, M.[Meng], Guo, Y.H.[Yu-Hong],
Progressive Ensemble Networks for Zero-Shot Recognition,
CVPR19(11720-11728).
IEEE DOI 2002
BibRef

Atzmon, Y.[Yuval], Chechik, G.[Gal],
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning,
CVPR19(11663-11672).
IEEE DOI 2002
BibRef

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
BibRef

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
BibRef

Hascoet, T.[Tristan], Ariki, Y.[Yasuo], Takiguchi, T.[Tetsuya],
On Zero-Shot Recognition of Generic Objects,
CVPR19(9545-9553).
IEEE DOI 2002
BibRef

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
BibRef

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],
Region Graph Embedding Network for Zero-shot Learning,
ECCV20(IV:562-580).
Springer DOI 2011
BibRef

Paul, A.[Akanksha], Krishnan, N.C.[Narayanan C.], Munjal, P.[Prateek],
Semantically Aligned Bias Reducing Zero Shot Learning,
CVPR19(7049-7058).
IEEE DOI 2002
BibRef

Ding, Z.M.[Zheng-Ming], Liu, H.F.[Hong-Fu],
Marginalized Latent Semantic Encoder for Zero-Shot Learning,
CVPR19(6184-6192).
IEEE DOI 2002
BibRef

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
BibRef

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
BibRef

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

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

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
Deep Few Shot Learning .


Last update:Jun 17, 2024 at 21:38:11