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],
Deep Multiple Instance Learning for Zero-Shot Image Tagging,
ACCV18(I:530-546).
Springer DOI
1906
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
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
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,
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 .