14.1.4.5 One Shot Learning, Few Shot Learning

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., Khan, S., 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

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.[Lixia], 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., Yu, X., Yu, A., Zhang, P., Wan, G., Wang, R.,
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

Woo, S.[Sanghyun], 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

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., Sun, X., Hong, X., Yao, Y., Wang, Y.,
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., Yang, X., Liu, Z., Qi, L., Zhou, H., Chiu, C.,
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.,
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

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, Three-dimensional, displays 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 .