14.1.8 One Shot Learning, Few Shot Learning

Chapter Contents (Back)
Small Sample Size. One-Shot Learning. Single Shot Learning. Few-Shot Learning. Once something is trained in some way, modify the rules with one sample.
See also Zero-Shot Learning.
See also Open Set Recongnition.

Fei-Fei, L.[Li], Fergus, R.[Rob], Perona, P.[Pietro],
One-Shot Learning of Object Categories,
PAMI(28), No. 4, April 2006, pp. 594-611.
IEEE DOI 0604
BibRef
Earlier:
A bayesian approach to unsupervised one-shot learning of object categories,
ICCV03(1134-1141).
IEEE DOI 0311
BibRef

Wang, G.[Gang], Zhang, Y.[Ye], Fei-Fei, L.[Li],
Using Dependent Regions for Object Categorization in a Generative Framework,
CVPR06(II: 1597-1604).
IEEE DOI 0606
BibRef

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

Rahman, S.[Shafin], Khan, S.[Salman], Barnes, N.,
Deep0Tag: Deep Multiple Instance Learning for Zero-Shot Image Tagging,
MultMed(22), No. 1, January 2020, pp. 242-255.
IEEE DOI 2001
BibRef
Earlier: A1, A2, Only:
Deep Multiple Instance Learning for Zero-Shot Image Tagging,
ACCV18(I:530-546).
Springer DOI 1906
Deep learning, Multiple instance learning, Feature pooling, Object detection, Zero-shot tagging BibRef

Zhuang, S.[Shuo], Wang, P.[Ping], Jiang, B.[Boran], Wang, G.[Gang], Wang, C.[Cong],
A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Zheng, Y.[Yan], Wang, R.[Ronggui], 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

Woo, S.H.[Sang-Hyun], Hwang, S.[Soonmin], Jang, H.D.[Ho-Deok], Kweon, I.S.[In So],
Gated bidirectional feature pyramid network for accurate one-shot detection,
MVA(30), No. 4, June 2019, pp. 543-555.
Springer DOI 1906
BibRef

Chen, Z., Fu, Y., Zhang, Y., Jiang, Y., Xue, X., Sigal, L.,
Multi-Level Semantic Feature Augmentation for One-Shot Learning,
IP(28), No. 9, Sep. 2019, pp. 4594-4605.
IEEE DOI 1908
computer vision, feature extraction, learning (artificial intelligence), semantic networks, vectors, feature augmentation BibRef

Sihag, S., Tajer, A.,
Optimal Network Parameter Estimation: Single-Shot Exchange of Local Decisions,
SPLetters(26), No. 9, September 2019, pp. 1280-1284.
IEEE DOI 1909
costing, estimation theory, iterative methods, least mean squares methods, mean square error methods, networks BibRef

Zhang, L.L.[Ling-Ling], Liu, J.[Jun], Luo, M.[Minnan], Chang, X.J.[Xiao-Jun], Zheng, Q.H.[Qing-Hua], Hauptmann, A.G.[Alexander G.],
Scheduled sampling for one-shot learning via matching network,
PR(96), 2019, pp. 106962.
Elsevier DOI 1909
Scheduled sampling, Matching network, From easy to difficult, One-shot learning, Difficulty metric 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.[Xiaobing], 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

Li, X.R.[Xi-Rong], Pu, F.L.[Fang-Ling], Yang, R.[Rui], Gui, R.[Rong], Xu, X.[Xin],
AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
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.[Huaxi], Zhang, J.[Junjie], 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.[Mingmin],
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

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.[Haozhe], 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.[Yifan], 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
computer vision, correlation methods, graph theory, image recognition 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.[Weihao], 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.[Kaijian], Ge, Y.[Yixiao], 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

Xu, C.M.[Cheng-Ming], Fu, Y.W.[Yan-Wei], Liu, C.[Chen], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Zhang, L.[Li], Xue, X.Y.[Xiang-Yang],
Learning Dynamic Alignment via Meta-filter for Few-shot Learning,
CVPR21(5178-5187)
IEEE DOI 2111
Visualization, Adaptation models, Semantics, Benchmark testing, Ordinary differential equations, Information filters BibRef

Chen, C.[Chaofan], Yang, X.[Xiaoshan], 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, Computer architecture, 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.[Trevor], 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.[Zhengyu], Ge, J.X.[Ji-Xie], Zhan, H.[Heshen], Huang, S.[Siteng], Wang, D.[Donglin],
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.[Zitian], Maji, S.[Subhransu], Learned-Miller, E.[Erik],
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

Xiao, C.X.[Chen-Xi], Madapana, N.[Naveen], Wachs, J.[Juan],
One-Shot Image Recognition Using Prototypical Encoders with Reduced Hubness,
WACV21(2251-2260)
IEEE DOI 2106
Measurement, Backpropagation, Visualization, Image recognition, Prototypes BibRef

Li, Z.[Zeqian], Mozer, M.[Michael], Whitehill, J.[Jacob],
Compositional Embeddings for Multi-Label One-Shot Learning,
WACV21(296-304)
IEEE DOI 2106
Training, Image recognition, Computational modeling, Supervised learning, Data models 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.[Gongjie], Cui, K.[Kaiwen], Wu, R.[Rongliang], Lu, S.[Shijian], 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, Computer architecture, Visual systems BibRef

Luo, Q.[Qinxuan], Wang, L.F.[Ling-Feng], Lv, J.[Jingguo], Xiang, S.M.[Shi-Ming], Pan, C.[Chunhong],
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
BibRef

Wang, H.J.[Hao-Jie], Lian, J.[Jieya], 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

Zhong, X.[Xian], Gu, C.[Cheng], Huang, W.X.[Wen-Xin], Li, L.[Lin], Chen, S.[Shuqin], 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

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.[Baoming], Zhou, C.[Chen], Zhao, B.[Bo], Guo, K.[Kan], Yang, J.[Jiang], Li, X.B.[Xiao-Bo], Zhang, M.[Ming], Wang, Y.[Yizhou],
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).
Springer DOI 2103
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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
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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).
Springer DOI 2011
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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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Guo, R.[Ronghao], Lin, C.[Chen], Li, C.[Chuming], Tian, K.[Keyu], Sun, M.[Ming], Sheng, L.[Lu], Yan, J.J.[Jun-Jie],
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy,
ECCV20(XIV:625-641).
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
BibRef

Liu, B.[Bin], Cao, Y.[Yue], Lin, Y.[Yutong], 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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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, Z.[Zichao], Zhang, X.Y.[Xiang-Yu], Mu, H.Y.[Hao-Yuan], Heng, W.[Wen], Liu, Z.[Zechun], Wei, Y.[Yichen], Sun, J.[Jian],
Single Path One-shot Neural Architecture Search with Uniform Sampling,
ECCV20(XVI: 544-560).
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

Liu, C., Xu, C., Wang, Y., Zhang, L., Fu, Y.,
An Embarrassingly Simple Baseline to One-shot Learning,
VL3W20(4005-4009)
IEEE DOI 2008
Training, Measurement, Task analysis, Testing, Machine learning, Support vector machines, Image recognition BibRef

Li, X., Lin, C., Li, C., Sun, M., Wu, W., Yan, J., Ouyang, W.,
Improving One-Shot NAS by Suppressing the Posterior Fading,
CVPR20(13833-13842)
IEEE DOI 2008
Computer architecture, Training, Fading channels, Bayes methods, Computational modeling, Data models, Search problems BibRef

Zhang, M., Li, H., Pan, S., Chang, X., Su, S.,
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization,
CVPR20(7806-7815)
IEEE DOI 2008
Computer architecture, Training, Task analysis, Optimization, Search methods, Solid modeling, Degradation BibRef

You, S., Huang, T., Yang, M., Wang, F., Qian, C., Zhang, C.,
GreedyNAS: Towards Fast One-Shot NAS With Greedy Supernet,
CVPR20(1996-2005)
IEEE DOI 2008
Training, Computer architecture, Task analysis, Graphics processing units, Hardware, Estimation BibRef

Zhang, C.[Chi], Cai, Y.J.[Yu-Jun], Lin, G.S.[Guo-Sheng], Shen, C.H.[Chun-Hua],
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers,
CVPR20(12200-12210)
IEEE DOI 2008
Optimal matching, Earth, Task analysis, Training, Measurement, Image representation, Neural networks 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, Computer architecture, Training, Neural networks, Adaptation models, Standards, Machine learning BibRef

Li, A., Huang, W., Lan, X., Feng, J., Li, Z., Wang, L.,
Boosting Few-Shot Learning With Adaptive Margin Loss,
CVPR20(12573-12581)
IEEE DOI 2008
Task analysis, Training, Semantics, Measurement, Additives, Mars, Generators BibRef

Wang, Y., Xu, C., Liu, C., Zhang, L., Fu, Y.,
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

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, Computer architecture, 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

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., Zhuo, W., Tang, C., Tai, Y.,
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, Computer vision 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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Chen, Z.[Zitian], Fu, Y.W.[Yan-Wei], Wang, Y.X.[Yu-Xiong], Ma, L.[Lin], Liu, W.[Wei], Hebert, M.[Martial],
Image Deformation Meta-Networks for One-Shot Learning,
CVPR19(8672-8681).
IEEE DOI 2002
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Kim, J.[Junsik], Oh, T.H.[Tae-Hyun], Lee, S.[Seokju], Pan, F.[Fei], Kweon, I.S.[In So],
Variational Prototyping-Encoder: One-Shot Learning With Prototypical Images,
CVPR19(9454-9462).
IEEE DOI 2002
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Zhang, H.[Hongguang], 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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Zhang, C.[Chi], Lin, G.[Guosheng], 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
BibRef

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.[Jiashi],
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
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Li, A.[Aoxue], Luo, T.[Tiange], Lu, Z.W.[Zhi-Wu], Xiang, T.[Tao], Wang, L.[Liwei],
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.[Jinglin], 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
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
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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
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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
BibRef

Kampffmeyer, M.[Michael], Chen, Y.[Yinbo], Liang, X.D.[Xiao-Dan], Wang, H.[Hao], Zhang, Y.[Yujia], 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
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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
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
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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.[Zhengming], Liu, H.[Hongfu],
Marginalized Latent Semantic Encoder for Zero-Shot Learning,
CVPR19(6184-6192).
IEEE DOI 2002
BibRef

Li, J.[Jin], Lan, X.[Xuguang], 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.[Pengkai], Wang, H.[Hanxiao], 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.[Changhu], 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
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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, H., Koniusz, P.,
Power Normalizing Second-Order Similarity Network for Few-Shot Learning,
WACV19(1185-1193)
IEEE DOI 1904
computer vision, higher order statistics, image capture, image recognition, learning (artificial intelligence), protocols, Image recognition 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, Computer vision 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

Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Compound Memory Networks for Few-Shot Video Classification,
ECCV18(VII: 782-797).
Springer DOI 1810
BibRef

Zhao, F.[Fang], Zhao, J.[Jian], Yan, S.C.[Shui-Cheng], Feng, J.[Jiashi],
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

Choi, J., Krishnamurthy, J., Kembhavi, A., Farhadi, A.,
Structured Set Matching Networks for One-Shot Part Labeling,
CVPR18(3627-3636)
IEEE DOI 1812
Labeling, Training, Task analysis, Visualization, Predictive models, Cognition, Semantics BibRef

Cai, Q., Pan, Y., Yao, T., Yan, C., Mei, T.,
Memory Matching Networks for One-Shot Image Recognition,
CVPR18(4080-4088)
IEEE DOI 1812
Training, Image recognition, Memory modules, Task analysis, Optimization, Knowledge engineering, Neural networks 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

Wang, P.[Peng], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], Huang, Z.[Zi], van den Hengel, A.J.[Anton J.], Shen, H.T.[Heng Tao],
Multi-attention Network for One Shot Learning,
CVPR17(6212-6220)
IEEE DOI 1711
Detectors, Feature extraction, Image recognition, Image representation, Semantics, Training, 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

Orrite, C.[Carlos], Rodriguez, M.[Mario], Medrano, C.[Carlos],
One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process,
ICPR16(2694-2699)
IEEE DOI 1705
Computational modeling, Encoding, Feature extraction, Hidden Markov models, Kernel, Videos BibRef

Sagawa, R., Shiba, Y., Hirukawa, T., Ono, S., Kawasaki, H., Furukawa, R.,
Automatic feature extraction using CNN for robust active one-shot scanning,
ICPR16(234-239)
IEEE DOI 1705
Cameras, Decoding, Encoding, Image color analysis, Image reconstruction, Shape, BibRef

Rodriguez, M.[Mario], Medrano, C.[Carlos], Herrero, E.[Elias], Orrite, C.[Carlos],
Spectral Clustering Using Friendship Path Similarity,
IbPRIA15(319-326).
Springer DOI 1506
BibRef

Yan, W.[Wang], Yap, J.[Jordan], Mori, G.[Greg],
Multi-Task Transfer Methods to Improve One-Shot Learning for Multimedia Event Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Tang, K.D.[Kevin D.], Tappen, M.F.[Marshall F.], Sukthankar, R.[Rahul], Lampert, C.H.[Christoph H.],
Optimizing one-shot recognition with micro-set learning,
CVPR10(3027-3034).
IEEE DOI 1006
Learn from single example. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Data Augmentation, Generative Network, Convolutional Network .


Last update:Nov 30, 2021 at 22:19:38