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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
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CVPR06(II: 1597-1604).
IEEE DOI
0606
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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,
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IEEE DOI
0507
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Learning with few examples for binary and multiclass classification
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Elsevier DOI
1101
BibRef
Earlier:
One-Shot Learning of Object Categories Using Dependent Gaussian
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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],
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A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot,
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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
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ACCV18(I:547-563).
Springer DOI
1906
BibRef
Rahman, S.[Shafin],
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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],
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A Single Shot Framework with Multi-Scale Feature Fusion for
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RS(11), No. 5, 2019, pp. xx-yy.
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BibRef
Zheng, Y.[Yan],
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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],
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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
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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
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],
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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
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CVS19(343-352).
Springer DOI
1912
BibRef
Chen, X.,
Wang, Y.,
Liu, J.,
Qiao, Y.,
DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot
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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
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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.X.[Hua-Xi],
Zhang, J.J.[Jun-Jie],
Zhang, J.[Jian],
Xu, J.S.[Jing-Song],
Wu, Q.[Qiang],
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot
Fine-Grained Image Classification,
MultMed(23), 2021, pp. 1666-1680.
IEEE DOI
2106
Feature extraction, Task analysis, Data models, Dogs,
Covariance matrices, Neural networks, Training, Bilinear pooling,
pairwise
BibRef
Liu, G.[Ge],
Zhao, L.[Linglan],
Fang, X.Z.[Xiang-Zhong],
PDA: Proxy-based domain adaptation for few-shot image recognition,
IVC(110), 2021, pp. 104164.
Elsevier DOI
2106
Few-shot image recognition, Domain adaptation,
Few-shot learning, Transfer learning
BibRef
Huang, H.W.[Hong-Wei],
Wu, Z.[Zhangkai],
Li, W.B.[Wen-Bin],
Huo, J.[Jing],
Gao, Y.[Yang],
Local descriptor-based multi-prototype network for few-shot Learning,
PR(116), 2021, pp. 107935.
Elsevier DOI
2106
Few-shot learning, Image classification, Local descriptors,
Multiple prototypes, End-to-end learning
BibRef
Ye, H.J.[Han-Jia],
Hum, H.X.[He-Xiang],
Zhan, D.C.[De-Chuan],
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot
Learning,
IJCV(129), No. 6, June 2021, pp. 1930-1953.
Springer DOI
2106
BibRef
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.M.[Ming-Min],
SAFFNet: Self-Attention-Based Feature Fusion Network for Remote
Sensing Few-Shot Scene Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
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.Z.[Hao-Zhe],
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
correlation methods, graph theory, image recognition
BibRef
Hu, Z.P.[Zheng-Ping],
Li, Z.J.[Zi-Jun],
Wang, X.[Xueyu],
Zheng, S.[Saiyue],
Unsupervised descriptor selection based meta-learning networks for
few-shot classification,
PR(122), 2022, pp. 108304.
Elsevier DOI
2112
Meta-learning, Few-shot classification,
Unsupervised localization, Descriptor selection
BibRef
Hu, Y.F.[Yu-Fan],
Gao, J.Y.[Jun-Yu],
Xu, C.S.[Chang-Sheng],
Learning Dual-Pooling Graph Neural Networks for Few-Shot Video
Classification,
MultMed(23), 2021, pp. 4285-4296.
IEEE DOI
2112
Task analysis, Feature extraction, Training, Testing,
Streaming media, Data models, Semantics, Few-shot learning,
video classification
BibRef
Cui, Y.[Yawen],
Liao, Q.[Qing],
Hu, D.[Dewen],
An, W.[Wei],
Liu, L.[Li],
Coarse-to-fine pseudo supervision guided meta-task optimization for
few-shot object classification,
PR(122), 2022, pp. 108296.
Elsevier DOI
2112
Unsupervised few-shot learning, Meta-learning, Clustering, Object classification
BibRef
Lin, C.C.[Chia-Ching],
Chu, H.L.[Hsin-Li],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Lei, C.L.[Chin-Laung],
Joint Feature Disentanglement and Hallucination for Few-Shot Image
Classification,
IP(30), 2021, pp. 9245-9258.
IEEE DOI
2112
Task analysis, Feature extraction, Visualization, Training,
Data models, Data mining, Birds, Few-shot learning (FSL),
feature disentanglement
BibRef
Zhang, L.[Lei],
Zuo, L.[Liyun],
Du, Y.J.[Ying-Jun],
Zhen, X.[Xiantong],
Learning to Adapt With Memory for Probabilistic Few-Shot Learning,
CirSysVideo(31), No. 11, November 2021, pp. 4283-4292.
IEEE DOI
2112
Task analysis, Adaptation models, Probabilistic logic,
Optimization, Neural networks, Prototypes, Predictive models,
variational inference
BibRef
Zhang, P.[Pei],
Fan, G.L.[Guo-Liang],
Wu, C.[Chanyue],
Wang, D.[Dong],
Li, Y.[Ying],
Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for
Few-Shot Remote Sensing Scene Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Label Independent Memory for Semi-Supervised Few-Shot Video
Classification,
PAMI(44), No. 1, January 2022, pp. 273-285.
IEEE DOI
2112
BibRef
Earlier:
Compound Memory Networks for Few-Shot Video Classification,
ECCV18(VII: 782-797).
Springer DOI
1810
Training, Feature extraction, Task analysis, Compounds, Dynamics,
Data models, Prototypes, Few-shot video classification,
compound memory networks
BibRef
Fu, K.[Kun],
Zhang, T.F.[Teng-Fei],
Zhang, Y.[Yue],
Wang, Z.R.[Zhi-Rui],
Sun, X.[Xian],
Few-Shot SAR Target Classification via Metalearning,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI
2112
Task analysis, Synthetic aperture radar, Training,
Target recognition, Adaptation models, Analytical models,
synthetic aperture radar (SAR)
BibRef
Zhang, L.[Lamei],
Zhang, S.[Siyu],
Zou, B.[Bin],
Dong, H.W.[Hong-Wei],
Unsupervised Deep Representation Learning and Few-Shot Classification
of PolSAR Images,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI
2112
Training, Task analysis, Optical imaging, Annotations,
Optical sensors, Neural networks, Training data,
unsupervised representation learning
BibRef
Huang, W.D.[Wen-Dong],
Yuan, Z.W.[Zheng-Wu],
Yang, A.X.[Ai-Xia],
Tang, C.[Chan],
Luo, X.B.[Xiao-Bo],
TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing
Scene Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Yuan, Z.W.[Zheng-Wu],
Huang, W.D.[Wen-Dong],
Tang, C.[Chan],
Yang, A.[Aixia],
Luo, X.B.[Xiao-Bo],
Graph-Based Embedding Smoothing Network for Few-Shot Scene
Classification of Remote Sensing Images,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link
2203
BibRef
Chen, C.[Cen],
Li, K.[Kenli],
Wei, W.[Wei],
Zhou, J.T.[Joey Tianyi],
Zeng, Z.[Zeng],
Hierarchical Graph Neural Networks for Few-Shot Learning,
CirSysVideo(32), No. 1, January 2022, pp. 240-252.
IEEE DOI
2201
Cognition, Feature extraction, Graph neural networks, Training,
Task analysis, Deep learning, Predictive models, Few-shot learning,
hierarchical structure
BibRef
Cao, C.Q.[Cong-Qi],
Zhang, Y.N.[Yan-Ning],
Learning to Compare Relation:
Semantic Alignment for Few-Shot Learning,
IP(31), 2022, pp. 1462-1474.
IEEE DOI
2202
Measurement, Streaming media, Task analysis, Semantics,
Mutual information, Uncertainty, Feature extraction, semantic alignment
BibRef
Ji, Z.[Zhong],
Hou, Z.S.[Zhi-Shen],
Liu, X.[Xiyao],
Pang, Y.W.[Yan-Wei],
Han, J.G.[Jun-Gong],
Information Symmetry Matters: A Modal-Alternating Propagation Network
for Few-Shot Learning,
IP(31), 2022, pp. 1520-1531.
IEEE DOI
2202
Semantics, Visualization, Task analysis, Training, Correlation, Sun,
Learning systems, Few-shot learning, meta-learning, multi-modal,
graph propagation
BibRef
Wu, S.[Shuang],
Kankanhalli, M.[Mohan],
Tung, A.K.H.[Anthony K.H.],
Superclass-aware network for few-shot learning,
CVIU(216), 2022, pp. 103349.
Elsevier DOI
2202
Few-shot learning, Contrastive loss, Feature attention
BibRef
Cheng, J.[Jun],
Hao, F.S.[Fu-Sheng],
Liu, L.[Liu],
Tao, D.C.[Da-Cheng],
Imposing Semantic Consistency of Local Descriptors for Few-Shot
Learning,
IP(31), 2022, pp. 1587-1600.
IEEE DOI
2202
Semantics, Training, Training data, Task analysis,
Convolutional neural networks, Adaptation models,
semantic consistency
BibRef
Li, F.[Feimo],
Li, S.[Shuaibo],
Fan, X.X.[Xin-Xin],
Li, X.[Xiong],
Chang, H.X.[Hong-Xing],
Structural Attention Enhanced Continual Meta-Learning for Graph Edge
Labeling Based Few-Shot Remote Sensing Scene Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Feng, L.J.[Liang-Jun],
Zhao, C.H.[Chun-Hui],
Li, X.[Xi],
Bias-Eliminated Semantic Refinement for Any-Shot Learning,
IP(31), 2022, pp. 2229-2244.
IEEE DOI
2203
Semantics, Training, Task analysis, Visualization, Generators,
Generative adversarial networks, Feature extraction,
modal alignment
BibRef
Zhang, B.[Bo],
Ye, H.[Hancheng],
Yu, G.[Gang],
Wang, B.[Bin],
Wu, Y.[Yike],
Fan, J.Y.[Jia-Yuan],
Chen, T.[Tao],
Sample-Centric Feature Generation for Semi-Supervised Few-Shot
Learning,
IP(31), 2022, pp. 2309-2320.
IEEE DOI
2203
Task analysis, Data models, Measurement, Training, Semantics,
Adaptation models, Benchmark testing, Few-shot learning,
sample-centric
BibRef
Zhang, L.L.[Ling-Ling],
Wang, S.[Shaowei],
Chang, X.J.[Xiao-Jun],
Liu, J.[Jun],
Ge, Z.[Zongyuan],
Zheng, Q.H.[Qing-Hua],
Auto-FSL: Searching the Attribute Consistent Network for Few-Shot
Learning,
CirSysVideo(32), No. 3, March 2022, pp. 1213-1223.
IEEE DOI
2203
Computer architecture, Training, Task analysis, Visualization,
Search problems, Neural networks, Network architecture,
DARTS
BibRef
Xu, D.[Dan],
Alameda-Pineda, X.[Xavier],
Ouyang, W.L.[Wan-Li],
Ricci, E.[Elisa],
Wang, X.G.[Xiao-Gang],
Sebe, N.[Nicu],
Probabilistic Graph Attention Network With Conditional Kernels for
Pixel-Wise Prediction,
PAMI(44), No. 5, May 2022, pp. 2673-2688.
IEEE DOI
2204
Predictive models, Semantics, Task analysis, Estimation,
Probabilistic logic, Kernel, Structured representation learning,
pixel-wise prediction
BibRef
Xu, B.R.[Bing-Rong],
Zeng, Z.G.[Zhi-Gang],
Lian, C.[Cheng],
Ding, Z.M.[Zheng-Ming],
Few-Shot Domain Adaptation via Mixup Optimal Transport,
IP(31), 2022, pp. 2518-2528.
IEEE DOI
2204
Adaptation models, Training, Numerical models, Couplings,
Feature extraction, Deep learning, Automation, Few-shot learning,
data augmentation
BibRef
Liang, M.J.[Ming-Jiang],
Huang, S.L.[Shao-Li],
Pan, S.R.[Shi-Rui],
Gong, M.M.[Ming-Ming],
Liu, W.[Wei],
Learning multi-level weight-centric features for few-shot learning,
PR(128), 2022, pp. 108662.
Elsevier DOI
2205
Fewshot learning, Low-shot learning, Multi-level features, Image classification
BibRef
Fu, W.[Wen],
Zhou, L.[Li],
Chen, J.[Jie],
Bidirectional Matching Prototypical Network for Few-Shot Image
Classification,
SPLetters(29), 2022, pp. 982-986.
IEEE DOI
2205
Prototypes, Training, Feature extraction, Image classification,
Task analysis, Predictive models, Measurement,
metric-based method
BibRef
Huang, J.[Jing],
Wu, B.[Bin],
Li, P.[Peng],
Li, X.[Xiao],
Wang, J.[Jie],
Few-Shot Learning for Radar Emitter Signal Recognition Based on
Improved Prototypical Network,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Cai, J.R.[Jia-Rui],
Wang, Y.Z.[Yi-Zhou],
Hwang, J.N.[Jenq-Neng],
ACE: Ally Complementary Experts for Solving Long-Tailed Recognition
in One-Shot,
ICCV21(112-121)
IEEE DOI
2203
Training, Representation learning, Codes, Computational modeling,
Linear programming, Classification algorithms,
BibRef
Li, S.[Suichan],
Chen, D.D.[Dong-Dong],
Chen, Y.P.[Yin-Peng],
Yuan, L.[Lu],
Zhang, L.[Lei],
Chu, Q.[Qi],
Liu, B.[Bin],
Yu, N.H.[Neng-Hai],
Improve Unsupervised Pretraining for Few-label Transfer,
ICCV21(10181-10190)
IEEE DOI
2203
Annotations, Computational modeling, Clustering algorithms,
Representation learning, Vision applications and systems
BibRef
Ma, J.W.[Jia-Wei],
Xie, H.[Hanchen],
Han, G.X.[Guang-Xing],
Chang, S.F.[Shih-Fu],
Galstyan, A.[Aram],
Abd-Almageed, W.[Wael],
Partner-Assisted Learning for Few-Shot Image Classification,
ICCV21(10553-10562)
IEEE DOI
2203
Training, Learning systems, Visualization, Annotations, Prototypes,
Benchmark testing, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Massiceti, D.[Daniela],
Zintgraf, L.[Luisa],
Bronskill, J.[John],
Theodorou, L.[Lida],
Harris, M.T.[Matthew Tobias],
Cutrell, E.[Edward],
Morrison, C.[Cecily],
Hofmann, K.[Katja],
Stumpf, S.[Simone],
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition,
ICCV21(10798-10808)
IEEE DOI
2203
Training, Technological innovation, Face recognition,
Benchmark testing, Orbits, Robustness, Datasets and evaluation,
Vision applications and systems
BibRef
Baik, S.[Sungyong],
Choi, J.[Janghoon],
Kim, H.[Heewon],
Cho, D.[Dohee],
Min, J.[Jaesik],
Lee, K.M.[Kyoung Mu],
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning,
ICCV21(9445-9454)
IEEE DOI
2203
Learning systems, Metals, Task analysis, Optimization,
Transfer/Low-shot/Semi/Unsupervised Learning,
Efficient training and inference methods
BibRef
Li, W.H.[Wei-Hong],
Liu, X.[Xialei],
Bilen, H.[Hakan],
Universal Representation Learning from Multiple Domains for Few-shot
Classification,
ICCV21(9506-9515)
IEEE DOI
2203
Uniform resource locators, Representation learning,
Knowledge engineering, Visualization, Computer aided instruction,
Recognition and classification
BibRef
Das, R.[Rajshekhar],
Wang, Y.X.[Yu-Xiong],
Moura, J.M.F.[José M. F.],
On the Importance of Distractors for Few-Shot Classification,
ICCV21(9010-9020)
IEEE DOI
2203
Training, Codes, Stochastic processes, Performance gain,
Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning,
Representation learning
BibRef
Afrasiyabi, A.[Arman],
Lalonde, J.F.[Jean-François],
Gagné, C.[Christian],
Mixture-based Feature Space Learning for Few-shot Image
Classification,
ICCV21(9021-9031)
IEEE DOI
2203
Training, Clustering algorithms, Mixture models,
Feature extraction, Classification algorithms, Standards,
Recognition and classification
BibRef
Phoo, C.P.[Cheng Perng],
Hariharan, B.[Bharath],
Coarsely-labeled Data for Better Few-shot Transfer,
ICCV21(9032-9041)
IEEE DOI
2203
Representation learning, Codes, Filtering, Buildings,
Transfer/Low-shot/Semi/Unsupervised Learning,
Representation learning
BibRef
Chen, Y.[Yinbo],
Liu, Z.[Zhuang],
Xu, H.J.[Hui-Juan],
Darrell, T.J.[Trevor J.],
Wang, X.L.[Xiao-Long],
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning,
ICCV21(9042-9051)
IEEE DOI
2203
Training, Measurement, Codes, Classification algorithms,
Task analysis, Standards,
BibRef
Chowdhury, A.[Arkabandhu],
Jiang, M.[Mingchao],
Chaudhuri, S.[Swarat],
Jermaine, C.[Chris],
Few-shot Image Classification: Just Use a Library of Pre-trained
Feature Extractors and a Simple Classifier,
ICCV21(9425-9434)
IEEE DOI
2203
Transfer learning, Feature extraction, Libraries,
Computational efficiency, Classification algorithms, Feeds,
Vision applications and systems
BibRef
Zhang, C.[Chi],
Ding, H.H.[Heng-Hui],
Lin, G.S.[Guo-Sheng],
Li, R.[Ruibo],
Wang, C.H.[Chang-Hu],
Shen, C.H.[Chun-Hua],
Meta Navigator:
Search for a Good Adaptation Policy for Few-shot Learning,
ICCV21(9415-9424)
IEEE DOI
2203
Adaptation models, Machine learning algorithms, Navigation,
Machine learning, Benchmark testing, Classification algorithms,
Recognition and classification
BibRef
Lazarou, M.[Michalis],
Stathaki, T.[Tania],
Avrithis, Y.[Yannis],
Iterative label cleaning for transductive and semi-supervised
few-shot learning,
ICCV21(8731-8740)
IEEE DOI
2203
Manifolds, Codes, Semisupervised learning, Prediction algorithms,
Cleaning, Inference algorithms,
Recognition and classification
BibRef
Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
Huang, M.Z.[Ming-Zhen],
Athar, S.[Shah_Rukh],
Samaras, D.[Dimitris],
Variational Feature Disentangling for Fine-Grained Few-Shot
Classification,
ICCV21(8792-8801)
IEEE DOI
2203
Codes, Lighting, Benchmark testing, Task analysis,
Image classification,
BibRef
Kang, D.[Dahyun],
Kwon, H.[Heeseung],
Min, J.[Juhong],
Cho, M.[Minsu],
Relational Embedding for Few-Shot Classification,
ICCV21(8802-8813)
IEEE DOI
2203
Training, Visualization, Tensors, Image recognition, Correlation,
Transforms, Transfer/Low-shot/Semi/Unsupervised Learning,
Representation learning
BibRef
Huang, K.[Kai],
Geng, J.[Jie],
Jiang, W.[Wen],
Deng, X.Y.[Xin-Yang],
Xu, Z.[Zhe],
Pseudo-loss Confidence Metric for Semi-supervised Few-shot Learning,
ICCV21(8651-8660)
IEEE DOI
2203
Measurement, Training, Weight measurement, Learning systems,
Estimation, Multitasking, Extraterrestrial measurements,
Recognition and classification
BibRef
Yang, L.[Lihe],
Zhuo, W.[Wei],
Qi, L.[Lei],
Shi, Y.H.[Ying-Huan],
Gao, Y.[Yang],
Mining Latent Classes for Few-shot Segmentation,
ICCV21(8701-8710)
IEEE DOI
2203
Training, Costs, Codes, Training data, Prototypes, Benchmark testing,
Transfer/Low-shot/Semi/Unsupervised Learning, Segmentation, grouping and shape
BibRef
Fei, N.Y.[Nan-Yi],
Gao, Y.Z.[Yi-Zhao],
Lu, Z.W.[Zhi-Wu],
Xiang, T.[Tao],
Z-Score Normalization, Hubness, and Few-Shot Learning,
ICCV21(142-151)
IEEE DOI
2203
Visualization, Prototypes, Benchmark testing, Boosting,
Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhang, X.[Xueting],
Meng, D.B.[De-Bin],
Gouk, H.[Henry],
Hospedales, T.[Timothy],
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition,
ICCV21(631-640)
IEEE DOI
2203
Training, Representation learning, Measurement, Uncertainty,
Memory management, Feature extraction,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yin, C.X.[Cheng-Xiang],
Wu, K.[Kun],
Che, Z.P.[Zheng-Ping],
Jiang, B.[Bo],
Xu, Z.Y.[Zhi-Yuan],
Tang, J.[Jian],
Hierarchical Graph Attention Network for Few-shot Visual-Semantic
Learning,
ICCV21(2157-2166)
IEEE DOI
2203
Integrated circuits, Deep learning, Visualization, Semantics,
Training data, Genomics, Vision + language,
Visual reasoning and logical representation
BibRef
Zhou, Z.Q.[Zi-Qi],
Qiu, X.[Xi],
Xie, J.T.[Jiang-Tao],
Wu, J.[Jianan],
Zhang, C.[Chi],
Binocular Mutual Learning for Improving Few-shot Classification,
ICCV21(8382-8391)
IEEE DOI
2203
Learning systems, Degradation, Computational modeling,
Decision making, Focusing, Performance gain,
Recognition and classification
BibRef
Qi, G.D.[Guo-Dong],
Yu, H.M.[Hui-Min],
Lu, Z.H.[Zhao-Hui],
Li, S.Z.[Shu-Zhao],
Transductive Few-Shot Classification on the Oblique Manifold,
ICCV21(8392-8402)
IEEE DOI
2203
Manifolds, Measurement, Machine learning, Benchmark testing,
Feature extraction, Approximation algorithms,
Recognition and classification
BibRef
Wu, J.[Jiamin],
Zhang, T.Z.[Tian-Zhu],
Zhang, Y.D.[Yong-Dong],
Wu, F.[Feng],
Task-aware Part Mining Network for Few-Shot Learning,
ICCV21(8413-8422)
IEEE DOI
2203
Adaptation models, Computational modeling, Benchmark testing,
Generators, Task analysis, Standards,
Recognition and classification
BibRef
Liu, Y.B.[Yan-Bin],
Lee, J.H.[Ju-Ho],
Zhu, L.C.[Lin-Chao],
Chen, L.[Ling],
Shi, H.[Humphrey],
Yang, Y.[Yi],
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification,
ICCV21(8433-8442)
IEEE DOI
2203
Training, Extrapolation, Correlation, Computational modeling,
Modulation, Information sharing,
BibRef
Osahor, U.[Uche],
Nasrabadi, N.M.[Nasser M.],
Ortho-Shot: Low Displacement Rank Regularization with Data
Augmentation for Few-Shot Learning,
WACV22(2040-2049)
IEEE DOI
2202
Computational modeling, Pipelines,
Diversity reception, Data models, Stability analysis,
Semi- and Un- supervised Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Lazarou, M.[Michalis],
Stathaki, T.[Tania],
Avrithis, Y.[Yannis],
Tensor feature hallucination for few-shot learning,
WACV22(2050-2060)
IEEE DOI
2202
Training, Representation learning, Tensors,
Focusing, Performance gain, Generative adversarial networks, GANs
BibRef
Garg, A.[Ashima],
Bagga, S.[Shaurya],
Singh, Y.[Yashvardhan],
Anand, S.[Saket],
HierMatch: Leveraging Label Hierarchies for Improving Semi-Supervised
Learning,
WACV22(2061-2070)
IEEE DOI
2202
Costs, Semisupervised learning, Benchmark testing,
Labeling, Deep Learning Transfer, Few-shot, Semi- and Un- supervised Learning
BibRef
Chowdhury, A.[Arkabandhu],
Chaudhari, D.[Dipak],
Chaudhuri, S.[Swarat],
Jermaine, C.[Chris],
Meta-Meta Classification for One-Shot Learning,
WACV22(1628-1637)
IEEE DOI
2202
Classification algorithms, Task analysis,
Transfer, Few-shot, Semi- and Un- supervised Learning ,
Image Processing
BibRef
Yang, F.Y.[Feng-Yuan],
Wang, R.P.[Rui-Ping],
Chen, X.L.[Xi-Lin],
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot
Learning,
WACV22(1586-1596)
IEEE DOI
2202
Training, Knowledge engineering, Visualization,
Correlation, Semantics, Prototypes, Transfer, Few-shot,
Semi- and Un- supervised Learning
BibRef
Bateni, P.[Peyman],
Barber, J.[Jarred],
van de Meent, J.W.[Jan-Willem],
Wood, F.[Frank],
Enhancing Few-Shot Image Classification with Unlabelled Examples,
WACV22(1597-1606)
IEEE DOI
2202
Training, Codes, Computational modeling,
Benchmark testing, Feature extraction, Data mining, Transfer,
Semi- and Un- supervised Learning
BibRef
Ye, C.G.[Chuang-Guan],
Zhu, H.Y.[Hong-Yuan],
Liao, Y.B.[Yong-Bin],
Zhang, Y.G.[Yang-Gang],
Chen, T.[Tao],
Fan, J.Y.[Jia-Yuan],
What Makes for Effective Few-shot Point Cloud Classification?,
WACV22(267-276)
IEEE DOI
2202
Point cloud compression, Deep learning, Solid modeling,
Adaptation models, Systematics,
3D Computer Vision
BibRef
Simon, C.[Christian],
Koniusz, P.[Piotr],
Harandi, M.[Mehrtash],
Meta-Learning for Multi-Label Few-Shot Classification,
WACV22(346-355)
IEEE DOI
2202
Microwave integrated circuits, Protocols,
Predictive models, Benchmark testing, Inference algorithms,
Semi- and Un- supervised Learning Deep Learning
BibRef
Yang, P.[Peng],
Ren, S.G.[Shao-Gang],
Zhao, Y.[Yang],
Li, P.[Ping],
Calibrating CNNs for Few-Shot Meta Learning,
WACV22(408-417)
IEEE DOI
2202
Training, Adaptation models, Neuroscience, Neurons,
Computer architecture, Benchmark testing, Calibration, Transfer,
Learning and Optimization
BibRef
Cui, Y.[Yawen],
Xiong, W.[Wuti],
Tavakolian, M.[Mohammad],
Liu, L.[Li],
Semi-Supervised Few-Shot Class-Incremental Learning,
ICIP21(1239-1243)
IEEE DOI
2201
Training, Image processing, Human intelligence, Benchmark testing,
Image classification, Few-shot learning, incremental learning.
BibRef
Zheng, P.X.[Pei-Xiao],
Guo, X.[Xin],
Qi, L.[Lin],
Edge-Labeling Based Directed Gated Graph Network for Few-Shot
Learning,
ICIP21(544-548)
IEEE DOI
2201
Backpropagation, Convolution, Image processing,
Image edge detection, Neural networks, Logic gates, CNN, GRU
BibRef
Liang, Z.Y.[Zi-Yun],
Gu, Y.[Yun],
Yang, J.[Jie],
Hardmix: A Regularization Method to Mitigate the Large Shift in
Few-Shot Domain Adaptation,
ICIP21(454-458)
IEEE DOI
2201
Training, Bridges, Image processing, Training data,
Benchmark testing, Classification algorithms, Domain Adaptation, Mix-Up
BibRef
Cheng, Y.C.[Yuan-Chia],
Lin, C.S.[Ci-Siang],
Yang, F.E.[Fu-En],
Wang, Y.C.F.[Yu-Chiang Frank],
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning
Across Visual Domains,
ICIP21(434-438)
IEEE DOI
2201
Training, Visualization, Image recognition, Target recognition,
Data models, Task analysis, few-shot learning
BibRef
Liu, S.[Sihan],
Wang, Y.[Yue],
Few-shot Learning with Online Self-Distillation,
VIPriors21(1067-1070)
IEEE DOI
2112
Training, Adaptation models, Pipelines, Benchmark testing, Data models
BibRef
Stojanov, S.[Stefan],
Thai, A.[Anh],
Rehg, J.M.[James M.],
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape
Bias,
CVPR21(1798-1808)
IEEE DOI
2111
Shape, Psychology,
Cognition, Pattern recognition, Object recognition
BibRef
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.X.[Yi-Xiao],
Ouyang, W.L.[Wan-Li],
Mutual CRF-GNN for Few-shot Learning,
CVPR21(2329-2339)
IEEE DOI
2111
Computational modeling, Semantics,
Benchmark testing, Probabilistic logic, Market research, Pattern recognition
BibRef
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.J.[Trevor J.],
Keutzer, K.[Kurt],
Vincentelli, A.S.[Alberto Sangiovanni],
Prototypical Cross-domain Self-supervised Learning for Few-shot
Unsupervised Domain Adaptation,
CVPR21(13829-13839)
IEEE DOI
2111
Semantics, Predictive models, Benchmark testing, Pattern recognition
BibRef
Chen, Z.[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.J.[Gong-Jie],
Cui, K.W.[Kai-Wen],
Wu, R.L.[Rong-Liang],
Lu, S.J.[Shi-Jian],
Tian, Y.H.[Yong-Hong],
PNPDet: Efficient Few-shot Detection without Forgetting via
Plug-and-Play Sub-networks,
WACV21(3822-3831)
IEEE DOI
2106
Measurement, Bridges, Detectors,
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.Y.[Jie-Ya],
Xiong, S.W.[Sheng-Wu],
Few-shot Learning with Unlabeled Outlier Exposure,
MMMod21(I:340-351).
Springer DOI
2106
BibRef
Matsumi, S.[Susumu],
Yamada, K.[Keiichi],
Few-Shot Learning Based on Metric Learning Using Class Augmentation,
ICPR21(196-201)
IEEE DOI
2105
Measurement, Support vector machines, Training data,
Machine learning, Nearest neighbor methods, Extraterrestrial measurements
BibRef
Wu, W.[Wei],
Pang, S.[Shanmin],
Tian, Z.Q.[Zhi-Qiang],
Li, Y.[Yaochen],
Meta Generalized Network for Few-Shot Classification,
ICPR21(1400-1405)
IEEE DOI
2105
Training, Measurement, Adaptation models, Image recognition,
Benchmark testing, Feature extraction, Pattern recognition
BibRef
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
Hamidouche, M.,
Lassance, C.[Carlos],
Hu, Y.,
Drumetz, L.,
Pasdeloup, B.,
Gripon, V.[Vincent],
Improving Classification Accuracy With Graph Filtering,
ICIP21(334-338)
IEEE DOI
2201
Training, Filtering, Training data, Machine learning,
Benchmark testing, Feature extraction, graph filtering,
deep learning
BibRef
Hu, Y.Q.[Yu-Qing],
Gripon, V.[Vincent],
Pateux, S.[Stéphane],
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot
Classification,
ICPR21(8164-8171)
IEEE DOI
2105
Interpolation, Feature extraction, Graph neural networks,
Pattern recognition, Standards, Logistics
BibRef
Yan, B.M.[Bao-Ming],
Zhou, C.[Chen],
Zhao, B.[Bo],
Guo, K.[Kan],
Yang, J.[Jiang],
Li, X.B.[Xiao-Bo],
Zhang, M.[Ming],
Wang, Y.Z.[Yi-Zhou],
Augmented Bi-path Network for Few-shot Learning,
ICPR21(8461-8468)
IEEE DOI
2105
Training, Visualization, Neural networks, Merging, Training data,
Feature extraction, Robustness
BibRef
Wang, Z.[Zhe],
Liu, L.[Li],
Li, F.[FanZhang],
TAAN: Task-Aware Attention Network for Few-shot Classification,
ICPR21(9130-9136)
IEEE DOI
2105
Training, Measurement, Transforms, Benchmark testing,
Feature extraction, Pattern recognition,
task-relevant channel attention
BibRef
Lifchitz, Y.[Yann],
Avrithis, Y.[Yannis],
Picard, S.[Sylvaine],
Local Propagation for Few-Shot Learning,
ICPR21(10457-10464)
IEEE DOI
2105
Image representation, Pattern recognition, Standards
BibRef
Cai, C.H.[Chun-Hao],
Yuan, M.L.[Ming-Lei],
Lu, T.[Tong],
IFSM: An Iterative Feature Selection Mechanism for Few-Shot Image
Classification,
ICPR21(9429-9436)
IEEE DOI
2105
Learning systems, Training data, Network architecture, Jitter,
Feature extraction, Reliability engineering, Pattern recognition,
feature selection
BibRef
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
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.H.[Rong-Hao],
Lin, C.[Chen],
Li, C.[Chuming],
Tian, K.Y.[Ke-Yu],
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
Monnier, T.[Tom],
Vincent, E.[Elliot],
Ponce, J.[Jean],
Aubry, M.[Mathieu],
Unsupervised Layered Image Decomposition into Object Prototypes,
ICCV21(8620-8630)
IEEE DOI
2203
Social networking (online), Computational modeling, Prototypes,
Predictive models, Benchmark testing, Image decomposition,
Visual reasoning and logical representation
BibRef
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, 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.[Aoxue],
Huang, W.R.[Wei-Ran],
Lan, X.[Xu],
Feng, J.S.[Jia-Shi],
Li, Z.G.[Zhen-Guo],
Wang, L.W.[Li-Wei],
Boosting Few-Shot Learning With Adaptive Margin Loss,
CVPR20(12573-12581)
IEEE DOI
2008
Task analysis, Training, Semantics, Measurement, Additives, Mars, Generators
BibRef
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
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.G.[Hong-Guang],
Zhang, J.[Jing],
Koniusz, P.[Piotr],
Few-Shot Learning via Saliency-Guided Hallucination of Samples,
CVPR19(2765-2774).
IEEE DOI
2002
BibRef
Wu, Z.H.[Zhong-Hua],
Shi, X.X.[Xiang-Xi],
Lin, G.S.[Guo-Sheng],
Cai, J.F.[Jian-Fei],
Learning Meta-class Memory for Few-Shot Semantic Segmentation,
ICCV21(497-506)
IEEE DOI
2203
Weight measurement, Training, Image quality, Image segmentation,
Fuses, Semantics, Prototypes, Recognition and classification, Scene analysis and understanding
BibRef
Zhang, C.[Chi],
Lin, G.S.[Guo-Sheng],
Liu, F.[Fayao],
Yao, R.[Rui],
Shen, C.H.[Chun-Hua],
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement
and Attentive Few-Shot Learning,
CVPR19(5212-5221).
IEEE DOI
2002
BibRef
Chu, W.H.[Wen-Hsuan],
Li, Y.J.[Yu-Jhe],
Chang, J.C.[Jing-Cheng],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image
Classification,
CVPR19(6244-6253).
IEEE DOI
2002
BibRef
Alfassy, A.[Amit],
Karlinsky, L.[Leonid],
Aides, A.[Amit],
Shtok, J.[Joseph],
Harary, S.[Sivan],
Feris, R.[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.S.[Jia-Shi],
Few-Shot Adaptive Faster R-CNN,
CVPR19(7166-7175).
IEEE DOI
2002
BibRef
Fei, N.Y.[Nan-Yi],
Guan, J.C.[Jie-Chao],
Lu, Z.W.[Zhi-Wu],
Gao, Y.Z.[Yi-Zhao],
Few-shot Zero-shot Learning: Knowledge Transfer with Less Supervision,
ACCV20(III:592-608).
Springer DOI
2103
BibRef
Li, A.[Aoxue],
Luo, T.[Tiange],
Lu, Z.W.[Zhi-Wu],
Xiang, T.[Tao],
Wang, L.[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.L.[Jing-Lin],
Huo, J.[Jing],
Gao, Y.[Yang],
Luo, J.B.[Jie-Bo],
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot
Learning,
CVPR19(7253-7260).
IEEE DOI
2002
BibRef
Schonfeld, E.[Edgar],
Ebrahimi, S.[Sayna],
Sinha, S.[Samarth],
Darrell, T.J.[Trevor J.],
Akata, Z.[Zeynep],
Generalized Zero- and Few-Shot Learning via Aligned Variational
Autoencoders,
CVPR19(8239-8247).
IEEE DOI
2002
BibRef
Dutta, A.[Anjan],
Mancini, M.[Massimiliano],
Akata, Z.[Zeynep],
Concurrent Discrimination and Alignment for Self-Supervised Feature
Learning,
DeepMTL21(2189-2198)
IEEE DOI
2112
Learning systems, Visualization, Protocols, Image recognition,
Transfer learning, Semantics, Benchmark testing
BibRef
Pastore, G.[Giuseppe],
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IEEE DOI
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Training, Image segmentation, Semantics, Pipelines,
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2002
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IEEE DOI
2002
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Progressive Ensemble Networks for Zero-Shot Recognition,
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IEEE DOI
2002
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Adaptive Confidence Smoothing for Generalized Zero-Shot Learning,
CVPR19(11663-11672).
IEEE DOI
2002
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CVPR19(11479-11488).
IEEE DOI
2002
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Hierarchical Disentanglement of Discriminative Latent Features for
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CVPR19(11459-11468).
IEEE DOI
2002
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On Zero-Shot Recognition of Generic Objects,
CVPR19(9545-9553).
IEEE DOI
2002
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Attentive Region Embedding Network for Zero-Shot Learning,
CVPR19(9376-9385).
IEEE DOI
2002
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Xie, G.S.[Guo-Sen],
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Zhao, F.[Fang],
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Region Graph Embedding Network for Zero-shot Learning,
ECCV20(IV:562-580).
Springer DOI
2011
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Semantically Aligned Bias Reducing Zero Shot Learning,
CVPR19(7049-7058).
IEEE DOI
2002
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Ding, Z.M.[Zheng-Ming],
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Marginalized Latent Semantic Encoder for Zero-Shot Learning,
CVPR19(6184-6192).
IEEE DOI
2002
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Compressing Unknown Images With Product Quantizer for Efficient
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IEEE DOI
2002
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Generalized Zero-Shot Recognition Based on Visually Semantic Embedding,
CVPR19(2990-2998).
IEEE DOI
2002
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Zero-Shot Task Transfer,
CVPR19(2184-2193).
IEEE DOI
2002
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Gradient Matching Generative Networks for Zero-Shot Learning,
CVPR19(2163-2173).
IEEE DOI
2002
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Huang, H.[He],
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Generative Dual Adversarial Network for Generalized Zero-Shot Learning,
CVPR19(801-810).
IEEE DOI
2002
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IEEE DOI
2002
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CVPR19(1-10).
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2002
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Edge-Labeling Graph Neural Network for Few-Shot Learning,
CVPR19(11-20).
IEEE DOI
2002
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Gidaris, S.[Spyros],
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Generating Classification Weights With GNN Denoising Autoencoders for
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CVPR19(21-30).
IEEE DOI
2002
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Schiele, B.[Bernt],
Meta-Transfer Learning for Few-Shot Learning,
CVPR19(403-412).
IEEE DOI
2002
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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
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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
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Zhang, H.,
Koniusz, P.,
Power Normalizing Second-Order Similarity Network for Few-Shot
Learning,
WACV19(1185-1193)
IEEE DOI
1904
higher order statistics, image capture,
image recognition, learning (artificial intelligence), protocols,
Image recognition
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Zhang, L.[Lu],
Yang, X.[Xu],
Liu, Z.Y.[Zhi-Yong],
Qi, L.[Lu],
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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
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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
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Gidaris, S.,
Komodakis, N.,
Dynamic Few-Shot Visual Learning Without Forgetting,
CVPR18(4367-4375)
IEEE DOI
1812
Training, Feature extraction, Generators, Training data,
Visualization, Object recognition, Task analysis
BibRef
Qiao, S.,
Liu, C.,
Shen, W.,
Yuille, A.L.,
Few-Shot Image Recognition by Predicting Parameters from Activations,
CVPR18(7229-7238)
IEEE DOI
1812
Training, Neural networks, Visualization, Training data, Linearity
BibRef
Wang, Y.,
Girshick, R.,
Hebert, M.,
Hariharan, B.,
Low-Shot Learning from Imaginary Data,
CVPR18(7278-7286)
IEEE DOI
1812
Training, Strain, Visualization, Data visualization, Task analysis,
Feature extraction, Machine vision
BibRef
Zhao, F.[Fang],
Zhao, J.[Jian],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Dynamic Conditional Networks for Few-Shot Learning,
ECCV18(XV: 20-36).
Springer DOI
1810
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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
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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
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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 .