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
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
Mazumder, P.[Pratik],
Singh, P.[Pravendra],
Namboodiri, V.P.[Vinay P.],
GIFSL: Grafting based improved few-shot learning,
IVC(104), 2020, pp. 104006.
Elsevier DOI
2012
Few-shot learning, Grafting, Self-supervision, Distillation,
Deep learning, Object recognition
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
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
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
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
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.[Rogerio],
A Broader Study of Cross-domain Few-shot Learning,
ECCV20(XXVII:124-141).
Springer DOI
2011
BibRef
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
BibRef
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
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
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
BibRef
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
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.[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
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
Sbai, O.[Othman],
Couprie, C.[Camille],
Aubry, M.[Mathieu],
Unsupervised Image Decomposition in Vector Layers,
ICIP20(1576-1580)
IEEE DOI
2011
Deep Image generation, unsupervised learning
BibRef
Sbai, O.[Othman],
Couprie, C.[Camille],
Aubry, M.[Mathieu],
Impact of Base Dataset Design on Few-shot Image Classification,
ECCV20(XVI: 597-613).
Springer DOI
2010
BibRef
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, 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
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
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, Computer vision, 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
Conferences, Computer vision, 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
BibRef
Seo, S.[Seonguk],
Seo, P.H.[Paul Hongsuck],
Han, B.H.[Bo-Hyung],
Learning for Single-Shot Confidence Calibration in Deep Neural Networks
Through Stochastic Inferences,
CVPR19(9022-9030).
IEEE DOI
2002
BibRef
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
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
BibRef
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
BibRef
Zhang, H.[Hongguang],
Zhang, J.[Jing],
Koniusz, P.[Piotr],
Few-Shot Learning via Saliency-Guided Hallucination of Samples,
CVPR19(2765-2774).
IEEE DOI
2002
BibRef
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
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.[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
BibRef
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
BibRef
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
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
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
Jamal, M.A.[Muhammad Abdullah],
Qi, G.J.[Guo-Jun],
Task Agnostic Meta-Learning for Few-Shot Learning,
CVPR19(11711-11719).
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.[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
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.[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
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.[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
BibRef
Li, H.Y.[Hong-Yang],
Eigen, D.[David],
Dodge, S.[Samuel],
Zeiler, M.[Matthew],
Wang, X.G.[Xiao-Gang],
Finding Task-Relevant Features for Few-Shot Learning by Category
Traversal,
CVPR19(1-10).
IEEE DOI
2002
BibRef
Kim, J.[Jongmin],
Kim, T.[Taesup],
Kim, S.[Sungwoong],
Yoo, C.D.[Chang D.],
Edge-Labeling Graph Neural Network for Few-Shot Learning,
CVPR19(11-20).
IEEE DOI
2002
BibRef
Gidaris, S.[Spyros],
Komodakis, N.[Nikos],
Generating Classification Weights With GNN Denoising Autoencoders for
Few-Shot Learning,
CVPR19(21-30).
IEEE DOI
2002
BibRef
Sun, Q.R.[Qian-Ru],
Liu, Y.Y.[Yao-Yao],
Chua, T.S.[Tat-Seng],
Schiele, B.[Bernt],
Meta-Transfer Learning for Few-Shot Learning,
CVPR19(403-412).
IEEE DOI
2002
BibRef
Pahde, F.,
Ostapenko, O.,
Hnichen, P.J.,
Klein, T.,
Nabi, M.,
Self-Paced Adversarial Training for Multimodal Few-Shot Learning,
WACV19(218-226)
IEEE DOI
1904
learning (artificial intelligence), neural nets,
object recognition, Oxford-102 dataset, fine grained CUB dataset,
Training data
BibRef
Mehrotra, A.,
Dukkipati, A.,
Skip Residual Pairwise Networks With Learnable Comparative Functions
for Few-Shot Learning,
WACV19(886-894)
IEEE DOI
1904
image representation, learning (artificial intelligence),
mini-Imagenet dataset, skip residual pairwise networks,
Data models
BibRef
Pahde, F.,
Puscas, M.,
Wolff, J.,
Klein, T.,
Sebe, N.,
Nabi, M.,
Low-Shot Learning From Imaginary 3D Model,
WACV19(978-985)
IEEE DOI
1904
image classification, learning (artificial intelligence),
neural nets, object recognition, set theory,
Meta-Learning
BibRef
Zhang, 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
Zhao, B.[Bo],
Sun, X.W.[Xin-Wei],
Hong, X.P.[Xiao-Peng],
Yao, Y.[Yuan],
Wang, Y.Z.[Yi-Zhou],
Zero-Shot Learning Via Recurrent Knowledge Transfer,
WACV19(1308-1317)
IEEE DOI
1904
graph theory, learning (artificial intelligence),
object recognition, pattern clustering, learned SSS,
Image edge detection
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
Douze, M.,
Szlam, A.,
Hariharan, B.,
Jégou, H.,
Low-Shot Learning with Large-Scale Diffusion,
CVPR18(3349-3358)
IEEE DOI
1812
Sparse matrices, Semisupervised learning, Visualization,
Diffusion processes, Training, Measurement, Image edge detection
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 .