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IEEE DOI
0406
Object recognition; Categorization; Generative model; Incremental learning;
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BibRef
Fei-Fei, L.[Li],
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A Bayesian Hierarchical Model for Learning Natural Scene Categories,
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BibRef
Earlier:
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Springer DOI
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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],
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Instance-Weighted Transfer Learning of Active Appearance Models,
CVPR14(1426-1433)
IEEE DOI
1409
active appearance models
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Elsevier DOI
1909
Few-shot learning, Metric learning, Feature attention, Complementary Cosine loss
BibRef
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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],
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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
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Song, Y.[Yu],
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MPPCANet: A Feedforward Learning Strategy for Few-Shot Image
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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
Huang, H.X.[Hua-Xi],
Zhang, J.J.[Jun-Jie],
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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
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
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
Jiang, N.[Nan],
Shi, H.[Haowen],
Geng, J.[Jie],
Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing
Image Scene Classification,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Zeng, Q.J.[Qing-Jie],
Geng, J.[Jie],
Task-specific contrastive learning for few-shot remote sensing image
scene classification,
PandRS(191), 2022, pp. 143-154.
Elsevier DOI
2208
Remote sensing image, Few-shot learning, Scene classification,
Contrastive learning
BibRef
Gong, H.Y.[Hui-Yun],
Wang, S.[Shuo],
Zhao, X.W.[Xiao-Wei],
Yan, Y.F.[Yi-Fan],
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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, 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
Feng, Y.B.[Yang-Bo],
Gao, J.Y.[Jun-Yu],
Xu, C.S.[Chang-Sheng],
Learning Dual-Routing Capsule Graph Neural Network for Few-Shot Video
Classification,
MultMed(25), 2023, pp. 3204-3216.
IEEE DOI
2309
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.Y.[Li-Yun],
Du, Y.J.[Ying-Jun],
Zhen, X.T.[Xian-Tong],
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
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Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Label Independent Memory for Semi-Supervised Few-Shot Video
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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
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
Cao, C.Q.[Cong-Qi],
Zhang, Y.N.[Yan-Ning],
Learning to Compare Relation:
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IP(31), 2022, pp. 1462-1474.
IEEE DOI
2202
Measurement, Streaming media, Task analysis, Semantics,
Mutual information, Uncertainty, Feature extraction, semantic alignment
BibRef
Wu, S.[Shuang],
Kankanhalli, M.S.[Mohan S.],
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
Hao, F.S.[Fu-Sheng],
He, F.X.[Feng-Xiang],
Cheng, J.[Jun],
Tao, D.C.[Da-Cheng],
Global-Local Interplay in Semantic Alignment for Few-Shot Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4351-4363.
IEEE DOI
2207
Semantics, Feature extraction, Measurement, Training,
Learning systems, Visualization, Cats, Few-shot learning,
global-local interplay
BibRef
Hao, F.S.[Fu-Sheng],
He, F.X.[Feng-Xiang],
Cheng, J.[Jun],
Wang, L.,
Cao, J.,
Tao, D.C.[Da-Cheng],
Collect and Select:
Semantic Alignment Metric Learning for Few-Shot Learning,
ICCV19(8459-8468)
IEEE DOI
2004
Code, Metric Learning.
WWW Link. image retrieval, learning (artificial intelligence),
multilayer perceptrons, tensors, 3D tensor,
Task analysis
BibRef
Zhang, B.[Bo],
Ye, H.C.[Han-Cheng],
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.W.[Shao-Wei],
Chang, X.J.[Xiao-Jun],
Liu, J.[Jun],
Ge, Z.Y.[Zong-Yuan],
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
Training, Task analysis, Visualization,
Search problems, Neural networks, Network architecture,
DARTS
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
Huang, J.[Jing],
Li, X.[Xiao],
Wu, B.[Bin],
Wu, X.Y.[Xin-Yu],
Li, P.[Peng],
Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced
Prototypical Network,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Cai, J.L.[Jin-Lei],
Zhang, Y.T.[Yue-Ting],
Guo, J.Y.[Jia-Yi],
Zhao, X.[Xin],
Lv, J.W.[Jun-Wei],
Hu, Y.X.[Yu-Xin],
ST-PN: A Spatial Transformed Prototypical Network for Few-Shot SAR
Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Zhou, Y.[Yuan],
Guo, Y.R.[Yan-Rong],
Hao, S.J.[Shi-Jie],
Hong, R.C.[Ri-Chang],
Hierarchical Prototype Refinement With Progressive Inter-Categorical
Discrimination Maximization for Few-Shot Learning,
IP(31), 2022, pp. 3414-3429.
IEEE DOI
2205
Prototypes, Training, Semantics, Visualization, Task analysis,
Interference, Correlation, Few-shot learning, metric learning,
inter-categorical discrimination
BibRef
Wang, J.Y.[Jia-Yan],
Wang, X.Q.[Xue-Qin],
Xing, L.[Lei],
Liu, B.D.[Bao-Di],
Li, Z.M.[Zong-Min],
Class-Shared SparsePCA for Few-Shot Remote Sensing Scene
Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Wang, Y.N.[Ya-Ning],
Liu, Z.J.[Zi-Jian],
Luo, Y.[Yang],
Luo, C.[Chunbo],
A transductive learning method to leverage graph structure for
few-shot learning,
PRL(159), 2022, pp. 189-195.
Elsevier DOI
2206
few-shot learning, clustering, semi-supervised learning, graph neural networks
BibRef
Chen, H.X.[Hao-Xing],
Li, H.X.[Hua-Xiong],
Li, Y.[Yaohui],
Chen, C.L.[Chun-Lin],
Shaping Visual Representations With Attributes for Few-Shot
Recognition,
SPLetters(29), 2022, pp. 1397-1401.
IEEE DOI
2207
Visualization, Training, Semantics, Prototypes, Task analysis, Sun,
Representation learning, Attribute-shaped learning,
attribute-visual attention
BibRef
Guo, Y.R.[Yu-Rong],
Du, R.[Ruoyi],
Li, X.X.[Xiao-Xu],
Xie, J.Y.[Ji-Yang],
Ma, Z.Y.[Zhan-Yu],
Dong, Y.[Yuan],
Learning Calibrated Class Centers for Few-Shot Classification by
Pair-Wise Similarity,
IP(31), 2022, pp. 4543-4555.
IEEE DOI
2207
Semantics, Measurement, Feature extraction, Correlation, Training,
Strain, Learning systems, Few-shot image classification,
query-guided mask
BibRef
Schwartz, E.[Eli],
Karlinsky, L.[Leonid],
Feris, R.S.[Rogerio S.],
Giryes, R.[Raja],
Bronstein, A.[Alex],
Baby steps towards few-shot learning with multiple semantics,
PRL(160), 2022, pp. 142-147.
Elsevier DOI
2208
BibRef
Xi, B.[Bobo],
Li, J.J.[Jiao-Jiao],
Li, Y.S.[Yun-Song],
Song, R.[Rui],
Hong, D.F.[Dan-Feng],
Chanussot, J.[Jocelyn],
Few-Shot Learning With Class-Covariance Metric for Hyperspectral
Image Classification,
IP(31), 2022, pp. 5079-5092.
IEEE DOI
2208
Measurement, Training, Task analysis, Euclidean distance,
Feature extraction, Iron, Hyperspectral imaging, Few-shot learning,
HSI classification
BibRef
Shao, S.[Shuai],
Xing, L.[Lei],
Xu, R.[Rui],
Liu, W.F.[Wei-Feng],
Wang, Y.J.[Yan-Jiang],
Liu, B.D.[Bao-Di],
MDFM: Multi-Decision Fusing Model for Few-Shot Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5151-5162.
IEEE DOI
2208
Feature extraction, Finite element analysis, Fuses, Dogs,
Data models, Birds, Adaptation models, Few-shot learning (FSL),
multi-decision fusing model (MDFM)
BibRef
Wu, J.Y.[Jia-Ying],
Hu, J.L.[Jing-Lu],
Redefining prior feature space via finetuning a triplet network for
few-shot learning,
IET-CV(16), No. 6, 2022, pp. 514-524.
DOI Link
2208
contrastive learning, few-shot learning, maximum a posteriori,
pretrained feature extractor, triplet network
BibRef
Wang, Y.K.[Yi-Kai],
Zhang, L.[Li],
Yao, Y.[Yuan],
Fu, Y.W.[Yan-Wei],
How to Trust Unlabeled Data? Instance Credibility Inference for
Few-Shot Learning,
PAMI(44), No. 10, October 2022, pp. 6240-6253.
IEEE DOI
2209
Training, Data models, Noise measurement, Task analysis,
Feature extraction, Visualization, Standards, Few-shot learning,
self-taught learning
BibRef
Wang, Y.K.[Yi-Kai],
Xu, C.M.[Cheng-Ming],
Liu, C.[Chen],
Zhang, L.[Li],
Fu, Y.W.[Yan-Wei],
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
Gao, F.[Fei],
Xu, J.M.[Jing-Ming],
Lang, R.L.[Rong-Ling],
Wang, J.[Jun],
Hussain, A.[Amir],
Zhou, H.Y.[Hui-Yu],
A Few-Shot Learning Method for SAR Images Based on Weighted Distance
and Feature Fusion,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Zhang, J.[Jing],
Zhang, X.Z.[Xin-Zhou],
Wang, Z.[Zhe],
Task Encoding With Distribution Calibration for Few-Shot Learning,
CirSysVideo(32), No. 9, September 2022, pp. 6240-6252.
IEEE DOI
2209
Task analysis, Feature extraction, Adaptation models, Calibration,
Encoding, Computational modeling, Training, Few-shot learning,
image classification
BibRef
Li, Z.J.[Zi-Jun],
Hu, Z.P.[Zheng-Ping],
Luo, W.W.[Wei-Wei],
Hu, X.[Xiao],
SaberNet: Self-attention based effective relation network for
few-shot learning,
PR(133), 2023, pp. 109024.
Elsevier DOI
2210
Few-shot learning, Feature representation, Task analysis, Transformers
BibRef
Yang, S.[Sai],
Liu, F.[Fan],
Chen, Z.[Zhiyu],
Feature hallucination in hypersphere space for few-shot
classification,
IET-IPR(16), No. 13, 2022, pp. 3603-3616.
DOI Link
2210
BibRef
Yang, S.[Shuo],
Wu, S.H.[Song-Hua],
Liu, T.L.[Tong-Liang],
Xu, M.[Min],
Bridging the Gap Between Few-Shot and Many-Shot Learning via
Distribution Calibration,
PAMI(44), No. 12, December 2022, pp. 9830-9843.
IEEE DOI
2212
Data models, Task analysis, Training, Mathematical models,
Calibration, Adaptation models, Gaussian distribution,
generalization error
BibRef
Huang, X.L.[Xi-Lang],
Choi, S.H.[Seon Han],
SAPENet: Self-Attention based Prototype Enhancement Network for
Few-shot Learning,
PR(135), 2023, pp. 109170.
Elsevier DOI
2212
Few-shot learning, Multi-head self-attention mechanism,
Image classification, -Nearest neighbor
BibRef
Xu, R.[Rui],
Xing, L.[Lei],
Shao, S.[Shuai],
Zhao, L.F.[Li-Fei],
Liu, B.[Baodi],
Liu, W.F.[Wei-Feng],
Zhou, Y.C.[Yi-Cong],
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8674-8687.
IEEE DOI
2212
Feature extraction, Data mining, Finite element analysis,
Training data, Semi-supervised learning, Few-shot learning,
graph co-training (GCT)
BibRef
Cui, Z.[Zhiyan],
Lu, N.[Na],
Wang, W.F.[Wei-Feng],
Guo, G.S.[Guang-Shuai],
Dual global-aware propagation for few-shot learning,
IVC(128), 2022, pp. 104574.
Elsevier DOI
2212
Few-shot learning, Label propagation, Global-aware features, Feature fusion
BibRef
Wu, J.Y.[Jia-Ying],
Hu, J.L.[Jing-Lu],
Learning a Latent Space with Triplet Network for Few-Shot Image
Classification,
ICPR22(5038-5044)
IEEE DOI
2212
Training data, Benchmark testing, Feature extraction,
Task analysis, Image classification
BibRef
Wang, R.Q.[Run-Qi],
Liu, Z.[Zhen],
Zhang, B.C.[Bao-Chang],
Guo, G.D.[Guo-Dong],
Doermann, D.[David],
Few-Shot Learning with Complex-Valued Neural Networks and Dependable
Learning,
IJCV(131), No. 1, January 2023, pp. 385-404.
Springer DOI
2301
BibRef
Xu, J.[Jian],
Liu, B.[Bo],
Xiao, Y.[Yanshan],
A Variational Inference Method for Few-Shot Learning,
CirSysVideo(33), No. 1, January 2023, pp. 269-282.
IEEE DOI
2301
Task analysis, Power capacitors, Estimation, Image synthesis,
Feature extraction, Training, Neural networks,
variational autoencoder (VAE)
BibRef
Wang, J.W.[Jun-Wen],
Gao, Y.B.[Yong-Bin],
Fang, Z.J.[Zhi-Jun],
An angular shrinkage BERT model for few-shot relation extraction with
none-of-the-above detection,
PRL(166), 2023, pp. 151-158.
Elsevier DOI
2302
Few-shot learning, Relation extraction, None-of-the-above detection
BibRef
Liu, X.Y.[Xin-Yue],
Liu, L.G.[Li-Gang],
Liu, H.[Han],
Zhang, X.T.[Xiao-Tong],
Capturing the few-shot class distribution:
Transductive distribution optimization,
PR(138), 2023, pp. 109371.
Elsevier DOI
2303
Few-shot learning, Transductive learning, Distribution estimation
BibRef
Liu, F.[Fan],
Li, F.F.[Fei-Fan],
Yang, S.[Sai],
Few-shot classification using Gaussianisation prototypical classifier,
IET-CV(17), No. 1, 2023, pp. 62-75.
DOI Link
2303
few-shot classification, maximum a posteriori, reliable prototype
BibRef
Li, W.B.[Wen-Bin],
Wang, L.[Lei],
Zhang, X.X.[Xing-Xing],
Qi, L.[Lei],
Huo, J.[Jing],
Gao, Y.[Yang],
Luo, J.B.[Jie-Bo],
Defensive Few-Shot Learning,
PAMI(45), No. 5, May 2023, pp. 5649-5667.
IEEE DOI
2304
Training, Task analysis, Image classification, Robustness,
Convolutional neural networks, Learning systems,
episodic training
BibRef
Qiang, W.W.[Wen-Wen],
Li, J.M.[Jiang-Meng],
Su, B.[Bing],
Fu, J.L.[Jian-Long],
Xiong, H.[Hui],
Wen, J.R.[Ji-Rong],
Meta Attention-Generation Network for Cross-Granularity Few-Shot
Learning,
IJCV(131), No. 5, May 2023, pp. 1211-1233.
Springer DOI
2305
BibRef
Shao, S.[Shuai],
Xing, L.[Lei],
Wang, Y.J.[Yan-Jiang],
Liu, B.[Baodi],
Liu, W.F.[Wei-Feng],
Zhou, Y.C.[Yi-Cong],
Attention-Based Multi-View Feature Collaboration for Decoupled
Few-Shot Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2357-2369.
IEEE DOI
2305
Collaboration, Feature extraction, Finite element analysis,
Task analysis, Training, Learning systems, Data models, self-attention block
BibRef
Xu, C.M.[Cheng-Ming],
Liu, C.[Chen],
Sun, X.W.[Xin-Wei],
Yang, S.[Siqian],
Wang, Y.[Yabiao],
Wang, C.J.[Cheng-Jie],
Fu, Y.W.[Yan-Wei],
PatchMix Augmentation to Identify Causal Features in Few-Shot
Learning,
PAMI(45), No. 6, June 2023, pp. 7639-7653.
IEEE DOI
2305
Correlation, Training, Dogs, Data models, Task analysis,
Image reconstruction, Training data, Few-shot learning,
intra-variance regularization
BibRef
Pan, M.H.[Mei-Hong],
Xin, H.Y.[Hong-Yi],
Xia, C.Q.[Chun-Qiu],
Shen, H.B.[Hong-Bin],
Few-shot classification with task-adaptive semantic feature learning,
PR(141), 2023, pp. 109594.
Elsevier DOI
2306
Few-shot learning, Multi-modality, Task-adaptive training,
Semantic feature learner
BibRef
Zhang, H.G.[Hong-Guang],
Li, H.D.[Hong-Dong],
Koniusz, P.[Piotr],
Multi-Level Second-Order Few-Shot Learning,
MultMed(25), 2023, pp. 2111-2126.
IEEE DOI
2306
BibRef
Earlier: A1, A3, Only:
Power Normalizing Second-Order Similarity Network for Few-Shot
Learning,
WACV19(1185-1193)
IEEE DOI
1904
Task analysis, Pipelines, Image recognition, Visualization,
Feature extraction, Training, Streaming media, Few-shot learning,
action recognition.
higher order statistics, image capture,
learning (artificial intelligence), protocols
BibRef
Tan, Q.[Qi],
Wu, Z.Z.[Zong-Ze],
Lai, J.L.[Jia-Lun],
Liang, Z.X.[Ze-Xiao],
Ren, Z.G.[Zhi-Gang],
HDGN: Heat diffusion graph network for few-shot learning,
PRL(171), 2023, pp. 61-68.
Elsevier DOI
2306
Few-shot learning, Graph convolution network, Low-pass filter,
Heat diffusion, Gait recognition, Image entropy, Multi-view recognition
BibRef
Shi, B.[Boyao],
Li, W.B.[Wen-Bin],
Huo, J.[Jing],
Zhu, P.F.[Peng-Fei],
Wang, L.[Lei],
Gao, Y.[Yang],
Global- and local-aware feature augmentation with semantic
orthogonality for few-shot image classification,
PR(142), 2023, pp. 109702.
Elsevier DOI
2307
Few-shot image classification, Transfer learning,
Feature augmentation, Semantic orthogonal learning
BibRef
Zhang, M.[Min],
Huang, S.[Siteng],
Li, W.B.[Wen-Bin],
Wang, D.L.[Dong-Lin],
Tree Structure-Aware Few-Shot Image Classification via Hierarchical
Aggregation,
ECCV22(XX:453-470).
Springer DOI
2211
BibRef
Chen, H.[Hao],
Li, L.Y.[Lin-Yan],
Hu, F.Y.[Fu-Yuan],
Lyu, F.[Fan],
Zhao, L.Q.[Liu-Qing],
Huang, K.Z.[Kai-Zhu],
Feng, W.[Wei],
Xia, Z.P.[Zhen-Ping],
Multi-semantic hypergraph neural network for effective few-shot
learning,
PR(142), 2023, pp. 109677.
Elsevier DOI
2307
Hypergraph, Few-shot learning, Multi-semantic learning, Orthogonal training
BibRef
Chen, J.J.[Jing-Jing],
Zhuo, L.H.[Lin-Hai],
Wei, Z.P.[Zhi-Peng],
Zhang, H.[Hao],
Fu, H.Z.[Hua-Zhu],
Jiang, Y.G.[Yu-Gang],
Knowledge driven weights estimation for large-scale few-shot image
recognition,
PR(142), 2023, pp. 109668.
Elsevier DOI
2307
Few-shot image, Recognition, Knowledge transfer
BibRef
Shao, Y.J.[Yuan-Jie],
Wu, W.X.[Wen-Xiao],
You, X.G.[Xin-Ge],
Gao, C.X.[Chang-Xin],
Sang, N.[Nong],
Improving the Generalization of MAML in Few-Shot Classification via
Bi-Level Constraint,
CirSysVideo(33), No. 7, July 2023, pp. 3284-3295.
IEEE DOI
2307
Adaptation models, Task analysis, Optimization, Measurement, Power capacitors,
Feature extraction, Data models, MAML, cross-task metric loss
BibRef
Zha, Z.[Zican],
Tang, H.[Hao],
Sun, Y.L.[Yun-Lian],
Tang, J.H.[Jin-Hui],
Boosting Few-Shot Fine-Grained Recognition With Background
Suppression and Foreground Alignment,
CirSysVideo(33), No. 8, August 2023, pp. 3947-3961.
IEEE DOI
2308
Task analysis, Measurement, Feature extraction, Birds, Annotations,
Training, Sun, Few-shot learning, fine-grained recognition,
foreground alignment
BibRef
Wang, S.M.[Shuang-Mei],
Ma, R.[Rui],
Wu, T.[Tieru],
Cao, Y.[Yang],
P3DC-shot: Prior-driven discrete data calibration for
nearest-neighbor few-shot classification,
IVC(136), 2023, pp. 104736.
Elsevier DOI
2308
Few-shot learning, Image classification, Prototype, Calibration
BibRef
Hu, Z.X.[Zi-Xuan],
Shen, L.[Li],
Lai, S.[Shenqi],
Yuan, C.[Chun],
Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot
Classification,
CirSysVideo(33), No. 8, August 2023, pp. 3638-3648.
IEEE DOI
2308
Task analysis, Bayes methods, Frequency division multiplexing,
Correlation, Uncertainty, Prototypes, Semantics,
Bayesian inference
BibRef
Dang, Z.H.[Zhuo-Hang],
Luo, M.[Minnan],
Jia, C.Y.[Cheng-You],
Yan, C.X.[Cai-Xia],
Chang, X.J.[Xiao-Jun],
Zheng, Q.H.[Qing-Hua],
Counterfactual Generation Framework for Few-Shot Learning,
CirSysVideo(33), No. 8, August 2023, pp. 3747-3758.
IEEE DOI
2308
Feature extraction, Data models, Task analysis, Prototypes,
Data mining, Semantics, Generators, Few-shot learning,
prototype learning
BibRef
Song, Y.S.[Yi-Sheng],
Wang, T.[Ting],
Cai, P.[Puyu],
Mondal, S.K.[Subrota K.],
Sahoo, J.P.[Jyoti Prakash],
A Comprehensive Survey of Few-Shot Learning:
Evolution, Applications, Challenges, and Opportunities,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
2309
Survey, Few-Shot Learning. prior knowledge, meta-learning, low-shot learning,
zero-shot learning, one-shot learning, Few-shot learning
BibRef
Cao, J.Z.[Jiang-Zhong],
Yao, Z.J.[Zi-Jie],
Yu, L.G.[Liang-Geng],
Ling, B.W.K.[Bingo Wing-Kuen],
WPE: Weighted prototype estimation for few-shot learning,
IVC(137), 2023, pp. 104757.
Elsevier DOI
2309
Few-shot learning, Knowledge transfer, Data augmentation,
Prototype estimation, Image classification
BibRef
Wu, Y.Q.[Ya-Qiang],
Li, Y.F.[Yi-Fei],
Zhao, T.Z.[Tian-Zhe],
Zhang, L.L.[Ling-Ling],
Wei, B.[Bifan],
Liu, J.[Jun],
Zheng, Q.H.[Qing-Hua],
Improved prototypical network for active few-shot learning,
PRL(172), 2023, pp. 188-194.
Elsevier DOI
2309
Few-shot learning, Active learning, Prototypical network,
Loss prediction, Image recognition
BibRef
Zhang, B.Q.[Bao-Quan],
Li, X.[Xutao],
Ye, Y.M.[Yun-Ming],
Feng, S.S.[Shan-Shan],
Prototype Completion for Few-Shot Learning,
PAMI(45), No. 10, October 2023, pp. 12250-12268.
IEEE DOI
2310
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
Fan, C.Y.[Chen-You],
Hu, J.J.[Jun-Jie],
Huang, J.W.[Jian-Wei],
Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning,
PAMI(45), No. 10, October 2023, pp. 11810-11823.
IEEE DOI
2310
BibRef
Xu, R.J.[Ren-Jie],
Xing, L.[Lei],
Liu, B.[Baodi],
Tao, D.P.[Da-Peng],
Cao, W.J.[Wei-Jia],
Liu, W.F.[Wei-Feng],
Cross-Domain Few-Shot classification via class-shared and
class-specific dictionaries,
PR(144), 2023, pp. 109811.
Elsevier DOI
2310
Few-shot learning, Dictionary learning, Cross-Domain,
Collaborative representation
BibRef
Walsh, R.[Reece],
Osman, I.[Islam],
Shehata, M.S.[Mohamed S.],
Masked Embedding Modeling With Rapid Domain Adjustment for Few-Shot
Image Classification,
IP(32), 2023, pp. 4907-4920.
IEEE DOI Code:
WWW Link.
2310
BibRef
Zhou, Z.Y.[Zhen-Yu],
Luo, L.[Lei],
Liao, Q.[Qing],
Liu, X.W.[Xin-Wang],
Zhu, E.[En],
Improving Embedding Generalization in Few-Shot Learning With Instance
Neighbor Constraints,
IP(32), 2023, pp. 5197-5208.
IEEE DOI
2310
BibRef
Qi, G.D.[Guo-Dong],
Long, Y.Q.[Yang-Qi],
Lu, Z.H.[Zhao-Hui],
Yu, H.M.[Hui-Min],
Causal Intervention for Few-Shot Hypothesis Adaptation,
SPLetters(30), 2023, pp. 1267-1271.
IEEE DOI
2310
BibRef
Xu, R.J.[Ren-Jie],
Yang, X.H.[Xing-Hao],
Yao, X.X.[Xing-Xing],
Tao, D.P.[Da-Peng],
Cao, W.J.[Wei-Jia],
Lu, X.P.[Xiao-Ping],
Liu, W.F.[Wei-Feng],
Self-Paced Hard Task-Example Mining for Few-Shot Classification,
CirSysVideo(33), No. 10, October 2023, pp. 5631-5644.
IEEE DOI
2310
BibRef
Wu, S.N.[Si-Ning],
Gao, X.[Xiang],
Hu, X.P.[Xiao-Peng],
Task-Oriented Feature Hallucination for Few-Shot Image Classification,
IET-IPR(17), No. 12, 2023, pp. 3564-3579.
DOI Link
2310
image classification, image recognition, image representation,
pattern recognition, supervised learning
BibRef
Deng, S.[Shule],
Yu, J.G.[Jin-Gang],
Wu, Z.[Zihao],
Gao, H.X.[Hong-Xia],
Li, Y.S.[Yan-Sheng],
Yang, Y.[Yang],
Learning Relative Feature Displacement for Few-Shot Open-Set
Recognition,
MultMed(25), 2023, pp. 5763-5774.
IEEE DOI
2311
BibRef
Liu, F.[Fan],
Yang, S.[Sai],
Chen, D.[Delong],
Huang, H.X.[Hua-Xi],
Zhou, J.[Jun],
Few-shot classification guided by generalization error bound,
PR(145), 2024, pp. 109904.
Elsevier DOI
2311
Few-shot classification, Generalization error bound,
Self-supervised learning, Knowledge distillation
BibRef
Shu, Y.[Yang],
Cao, Z.J.[Zhang-Jie],
Gao, J.H.[Jing-Han],
Wang, J.M.[Jian-Min],
Yu, P.S.[Philip S.],
Long, M.S.[Ming-Sheng],
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot
Learning,
PAMI(45), No. 12, December 2023, pp. 15275-15291.
IEEE DOI
2311
BibRef
Li, W.B.[Wen-Bin],
Wang, Z.[Ziyi],
Yang, X.S.[Xue-Song],
Dong, C.[Chuanqi],
Tian, P.[Pinzhuo],
Qin, T.[Tiexin],
Huo, J.[Jing],
Shi, Y.[Yinghuan],
Wang, L.[Lei],
Gao, Y.[Yang],
Luo, J.B.[Jie-Bo],
LibFewShot: A Comprehensive Library for Few-Shot Learning,
PAMI(45), No. 12, December 2023, pp. 14938-14955.
IEEE DOI
2311
BibRef
Tian, P.Z.[Pin-Zhuo],
Xie, S.R.[Shao-Rong],
An Adversarial Meta-Training Framework for Cross-Domain Few-Shot
Learning,
MultMed(25), 2023, pp. 6881-6891.
IEEE DOI
2311
BibRef
Clay, V.[Viviane],
Pipa, G.[Gordon],
Kühnberger, K.U.[Kai-Uwe],
König, P.[Peter],
Development of Few-Shot Learning Capabilities in Artificial Neural
Networks When Learning Through Self-Supervised Interaction,
PAMI(46), No. 1, January 2024, pp. 209-219.
IEEE DOI
2312
BibRef
Park, S.[Sangwoo],
Cohen, K.M.[Kfir M.],
Simeone, O.[Osvaldo],
Few-Shot Calibration of Set Predictors via Meta-Learned
Cross-Validation-Based Conformal Prediction,
PAMI(46), No. 1, January 2024, pp. 280-291.
IEEE DOI
2312
BibRef
Sun, J.X.[Jia-Xing],
Shen, X.B.[Xiao-Bo],
Sun, Q.S.[Quan-Sen],
Efficient Feature Reconstruction via l2,1-Norm Regularization for
Few-Shot Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7452-7465.
IEEE DOI
2312
BibRef
Wang, X.X.[Xi-Xi],
Wang, X.[Xiao],
Jiang, B.[Bo],
Luo, B.[Bin],
Few-Shot Learning Meets Transformer: Unified Query-Support
Transformers for Few-Shot Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7789-7802.
IEEE DOI
2312
BibRef
Hamzaoui, M.[Manal],
Chapel, L.[Laetitia],
Pham, M.T.[Minh-Tan],
Lefèvre, S.[Sébastien],
Hyperbolic prototypical network for few shot remote sensing scene
classification,
PRL(177), 2024, pp. 151-156.
Elsevier DOI
2401
Few-shot learning, Hyperbolic space, Scene classification, Remote sensing
BibRef
Li, Q.[Qiaonan],
Wen, G.H.[Gui-Hua],
Yang, P.[Pei],
From patch, sample to domain: Capture geometric structures for
few-shot learning,
PR(148), 2024, pp. 110147.
Elsevier DOI
2402
Cross-domain, Few-shot learning, Optimal transport
BibRef
Gao, R.X.[Rui-Xuan],
Su, H.[Han],
Prasad, S.[Shitala],
Tang, P.[Peisen],
Few-shot classification with multisemantic information fusion network,
IVC(141), 2024, pp. 104869.
Elsevier DOI
2402
Few-shot learning, Metric-based learning,
Feature representation, Unsupervised mechanism
BibRef
Ye, H.J.[Han-Jia],
Ming, L.[Lu],
Zhan, D.C.[De-Chuan],
Chao, W.L.[Wei-Lun],
Few-Shot Learning With a Strong Teacher,
PAMI(46), No. 3, March 2024, pp. 1425-1440.
IEEE DOI
2402
Task analysis, Training, Feature extraction, Benchmark testing,
Visualization, Standards, Loss measurement, Few-shot learning,
knowledge distillation
BibRef
Feng, R.[Rui],
Ji, H.B.[Hong-Bing],
Zhu, Z.G.[Zhi-Gang],
Wang, L.[Lei],
Global Information Embedding Network for Few-Shot Learning,
SPLetters(31), 2024, pp. 501-505.
IEEE DOI
2402
Feature extraction, Task analysis, Prototypes,
Frequency-domain analysis, Training, Benchmark testing, contrastive learning
BibRef
Shao, S.[Shuai],
Wang, Y.[Yan],
Liu, B.[Bin],
Liu, W.F.[Wei-Feng],
Wang, Y.J.[Yag-Jiang],
Liu, B.[Baodi],
FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot
Classification,
CirSysVideo(34), No. 2, February 2024, pp. 839-851.
IEEE DOI
2402
Data augmentation, Power capacitors, Frequency-domain analysis,
Discrete Fourier transforms, Task analysis, Semantics, Data models,
Fourier-augmentation based data-shunting
BibRef
Han, M.Y.[Meng-Ya],
Zhan, Y.B.[Yi-Bing],
Luo, Y.[Yong],
Hu, H.[Han],
Su, K.[Kehua],
Du, B.[Bo],
Textual Enhanced Adaptive Meta-Fusion for Few-Shot Visual Recognition,
MultMed(26), 2024, pp. 2408-2418.
IEEE DOI
2402
Visualization, Semantics, Task analysis, Metalearning, Training,
Standards, Feature extraction, Few-shot visual recognition, multimodal fusion
BibRef
Dong, Z.[Zhong],
Lin, B.[Baojun],
Xie, F.[Fang],
Optimizing Few-Shot Remote Sensing Scene Classification Based on an
Improved Data Augmentation Approach,
RS(16), No. 3, 2024, pp. 525.
DOI Link
2402
BibRef
Wei, X.S.[Xiu-Shen],
Xu, H.Y.[He-Yang],
Yang, Z.W.[Zhi-Wen],
Duan, C.L.[Chen-Long],
Peng, Y.X.[Yu-Xin],
Negatives Make a Positive: An Embarrassingly Simple Approach to
Semi-Supervised Few-Shot Learning,
PAMI(46), No. 4, April 2024, pp. 2091-2103.
IEEE DOI
2403
Multiple signal classification, Task analysis, Training,
Predictive models, Data models, Computational modeling, Robustness,
few-shot learning
BibRef
Zheng, P.X.[Pei-Xiao],
Guo, X.[Xin],
Chen, E.[Enqing],
Qi, L.[Lin],
Guan, L.[Ling],
Edge-labeling based modified gated graph network for few-shot learning,
PR(150), 2024, pp. 110264.
Elsevier DOI Code:
WWW Link.
2403
BibRef
Earlier: A1, A2, A4, Only:
Edge-Labeling Based Directed Gated Graph Network for Few-Shot
Learning,
ICIP21(544-548)
IEEE DOI
2201
Graph network, Few-shot learning, Gated recurrent unit, Edge-labeling.
Backpropagation, Convolution,
Image edge detection, Neural networks, Logic gates, CNN, GRU
BibRef
Xia, H.F.[Hai-Feng],
Li, K.[Kai],
Min, M.R.Q.[Martin Ren-Qiang],
Ding, Z.M.[Zheng-Ming],
Few-Shot Video Classification via Representation Fusion and Promotion
Learning,
ICCV23(19254-19263)
IEEE DOI
2401
BibRef
Yi, X.Y.[Xuan-Yu],
Deng, J.J.[Jia-Jun],
Sun, Q.[Qianru],
Hua, X.S.[Xian-Sheng],
Lim, J.H.[Joo-Hwee],
Zhang, H.W.[Han-Wang],
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud
Recognition,
ICCV23(14417-14428)
IEEE DOI
2401
BibRef
Hao, F.S.[Fu-Sheng],
He, F.X.[Feng-Xiang],
Liu, L.[Liu],
Wu, F.X.[Fu-Xiang],
Tao, D.C.[Da-Cheng],
Cheng, J.[Jun],
Class-Aware Patch Embedding Adaptation for Few-Shot Image
Classification,
ICCV23(18859-18869)
IEEE DOI Code:
WWW Link.
2401
BibRef
Hu, T.[Teng],
Zhang, J.N.[Jiang-Ning],
Liu, L.[Liang],
Yi, R.[Ran],
Kou, S.Q.[Si-Qi],
Zhu, H.[Haokun],
Chen, X.[Xu],
Wang, Y.[Yabiao],
Wang, C.J.[Cheng-Jie],
Ma, L.Z.[Li-Zhuang],
Phasic Content Fusing Diffusion Model with Directional Distribution
Consistency for Few-Shot Model Adaption,
ICCV23(2406-2415)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lazarou, M.[Michalis],
Avrithis, Y.[Yannis],
Ren, G.Y.[Guang-Yu],
Stathaki, T.[Tania],
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning,
ICIP23(331-335)
IEEE DOI Code:
WWW Link.
2312
BibRef
Zhu, J.J.[Jun-Jie],
Yang, K.[Ke],
Qiu, C.P.[Chun-Ping],
Dai, M.Y.[Meng-Yuan],
Guan, N.Y.[Nai-Yang],
Yi, X.D.[Xiao-Dong],
Hybrid Contrastive Prototypical Network for Few-Shot Scene
Classification,
ICIP23(3588-3592)
IEEE DOI
2312
BibRef
Sun, L.[Li],
Wang, L.[Liuan],
Sun, J.[Jun],
Okatani, T.[Takayuki],
Prompt Prototype Learning Based on Ranking Instruction For Few-Shot
Visual Tasks,
ICIP23(3235-3239)
IEEE DOI
2312
BibRef
Xu, H.[Huali],
Zhi, S.F.[Shuai-Feng],
Liu, L.[Li],
Cross-Domain Few-Shot Classification Via Inter-Source Stylization,
ICIP23(565-569)
IEEE DOI
2312
BibRef
Trosten, D.J.[Daniel J.],
Chakraborty, R.[Rwiddhi],
Løksc, S.[Sigurd],
Wickstrøm, K.K.[Kristoffer Knutsen],
Jenssen, R.[Robert],
Kampffmeyer, M.C.[Michael C.],
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive
Few-Shot Learning with Hyperspherical Embeddings,
CVPR23(7527-7536)
IEEE DOI
2309
BibRef
Chen, W.T.[Wen-Tao],
Si, C.Y.[Chen-Yang],
Zhang, Z.[Zhang],
Wang, L.[Liang],
Wang, Z.[Zilei],
Tan, T.N.[Tie-Niu],
Semantic Prompt for Few-Shot Image Recognition,
CVPR23(23581-23591)
IEEE DOI
2309
BibRef
Zhu, H.[Hao],
Koniusz, P.[Piotr],
Transductive Few-Shot Learning with Prototype-Based Label Propagation
by Iterative Graph Refinement,
CVPR23(23996-24006)
IEEE DOI
2309
BibRef
Boudiaf, M.[Malik],
Bennequin, E.[Etienne],
Tami, M.[Myriam],
Toubhans, A.[Antoine],
Piantanida, P.[Pablo],
Hudelot, C.[Celine],
Ayed, I.B.[Ismail Ben],
Open-Set Likelihood Maximization for Few-Shot Learning,
CVPR23(24007-24016)
IEEE DOI
2309
BibRef
Anvekar, T.[Tejas],
Bazazian, D.[Dena],
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot
Learning,
DLGC23(4179-4188)
IEEE DOI
2309
BibRef
Parmar, V.[Vivek],
Kingra, S.K.[Sandeep Kaur],
Sarwar, S.S.[Syed Shakib],
Li, Z.[Ziyun],
de Salvo, B.[Barbara],
Suri, M.[Manan],
Fully-Binarized Distance Computation based On-device Few-Shot
Learning for XR applications,
EVW23(4502-4508)
IEEE DOI
2309
BibRef
Padmanabhan, D.C.[Deepan Chakravarthi],
Gowda, S.[Shruthi],
Arani, E.[Elahe],
Zonooz, B.[Bahram],
LSFSL: Leveraging Shape Information in Few-shot Learning,
L3D-IVU23(4971-4980)
IEEE DOI
2309
BibRef
Wang, R.Q.[Run-Qi],
Zheng, H.[Hao],
Duan, X.Y.[Xiao-Yue],
Liu, J.Z.[Jian-Zhuang],
Lu, Y.N.[Yu-Ning],
Wang, T.[Tian],
Xu, S.[Songcen],
Zhang, B.C.[Bao-Chang],
Few-Shot Learning with Visual Distribution Calibration and
Cross-Modal Distribution Alignment,
CVPR23(23445-23454)
IEEE DOI
2309
BibRef
Zhou, F.[Fei],
Wang, P.[Peng],
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Revisiting Prototypical Network for Cross Domain Few-Shot Learning,
CVPR23(20061-20070)
IEEE DOI
2309
BibRef
Ma, T.Y.[Tian-Yi],
Sun, Y.F.[Yi-Fan],
Yang, Z.X.[Zong-Xin],
Yang, Y.[Yi],
ProD: Prompting-to-disentangle Domain Knowledge for Cross-domain
Few-shot Image Classification,
CVPR23(19754-19763)
IEEE DOI
2309
BibRef
Zhang, R.[Renrui],
Hu, X.F.[Xiang-Fei],
Li, B.[Bohao],
Huang, S.Y.[Si-Yuan],
Deng, H.Q.[Han-Qiu],
Qiao, Y.[Yu],
Gao, P.[Peng],
Li, H.S.[Hong-Sheng],
Prompt, Generate, Then Cache: Cascade of Foundation Models Makes
Strong Few-Shot Learners,
CVPR23(15211-15222)
IEEE DOI
2309
BibRef
Zhang, H.G.[Hong-Guang],
Torr, P.H.S.[Philip H. S.],
Koniusz, P.[Piotr],
Improving Few-shot Learning by Spatially-aware Matching and
Crosstransformer,
ACCV22(V:3-20).
Springer DOI
2307
BibRef
Song, K.[Kun],
Wu, Y.C.[Yu-Chen],
Chen, J.S.[Jian-Sheng],
Hu, T.Y.[Tian-Yu],
Ma, H.M.[Hui-Min],
Gestalt-guided Image Understanding for Few-shot Learning,
ACCV22(II:409-424).
Springer DOI
2307
BibRef
Sendera, M.[Marcin],
Przewiezlikowski, M.[Marcin],
Karanowski, K.[Konrad],
Zieba, M.[Maciej],
Tabor, J.[Jacek],
Spurek, P.[Przemyslaw],
HyperShot: Few-Shot Learning by Kernel HyperNetworks,
WACV23(2468-2477)
IEEE DOI
2302
Training, Adaptation models, Computational modeling, Switches,
Predictive models, Planning, and algorithms (including transfer)
BibRef
He, J.[Ju],
Kortylewski, A.[Adam],
Yuille, A.L.[Alan L.],
CORL: Compositional Representation Learning for Few-Shot
Classification,
WACV23(3879-3888)
IEEE DOI
2302
Training, Representation learning, Dictionaries, Image recognition,
Knowledge based systems, Neural networks,
and algorithms (including transfer)
BibRef
He, X.[Xi],
Li, F.Z.[Fan-Zhang],
Task-adaptive Few-shot Learning on Sphere Manifold,
ICPR22(2949-2956)
IEEE DOI
2212
Manifolds, Learning systems, Technological innovation,
Euclidean distance, Benchmark testing, Pattern recognition
BibRef
Ma, Y.X.[Yi-Xiao],
Li, F.Z.[Fan-Zhang],
Self-Challenging Mask for Cross-Domain Few-Shot Classification,
ICPR22(4456-4453)
IEEE DOI
2212
Measurement, Visualization, Analytical models, Feature extraction,
Robustness, Power capacitors
BibRef
Lu, Y.N.[Yu-Ning],
Wen, L.J.[Liang-Jian],
Liu, J.Z.[Jian-Zhuang],
Liu, Y.J.[Ya-Jing],
Tian, X.[Xinmei],
Self-Supervision Can Be a Good Few-Shot Learner,
ECCV22(XIX:740-758).
Springer DOI
2211
BibRef
Zhang, T.[Tao],
Huang, W.[Wu],
Kernel Relative-prototype Spectral Filtering for Few-Shot Learning,
ECCV22(XX:541-557).
Springer DOI
2211
BibRef
Nguyen, K.D.[Khoi D.],
Tran, Q.H.[Quoc-Huy],
Nguyen, K.[Khoi],
Hua, B.S.[Binh-Son],
Nguyen, R.[Rang],
Inductive and Transductive Few-Shot Video Classification via Appearance
and Temporal Alignments,
ECCV22(XX:471-487).
Springer DOI
2211
BibRef
Chen, W.T.[Wen-Tao],
Zhang, Z.[Zhang],
Wang, W.[Wei],
Wang, L.[Liang],
Wang, Z.[Zilei],
Tan, T.N.[Tie-Niu],
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and
Aligned Representations,
ECCV22(XX:383-399).
Springer DOI
2211
BibRef
Dong, B.[Bowen],
Zhou, P.[Pan],
Yan, S.C.[Shui-Cheng],
Zuo, W.M.[Wang-Meng],
Self-Promoted Supervision for Few-Shot Transformer,
ECCV22(XX:329-347).
Springer DOI
2211
BibRef
Yang, Z.Y.[Zhan-Yuan],
Wang, J.H.[Jing-Hua],
Zhu, Y.Y.[Ying-Ying],
Few-Shot Classification with Contrastive Learning,
ECCV22(XX:293-309).
Springer DOI
2211
BibRef
Li, H.Q.[Hao-Quan],
Zhang, L.[Laoming],
Zhang, D.[Daoan],
Fu, L.[Lang],
Yang, P.[Peng],
Zhang, J.G.[Jian-Guo],
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot
Learning,
ECCV22(XX:524-540).
Springer DOI
2211
BibRef
Xiang, X.[Xiang],
Tan, Y.[Yuwen],
Wan, Q.[Qian],
Ma, J.[Jing],
Yuille, A.L.[Alan L.],
Hager, G.D.[Gregory D.],
Coarse-To-Fine Incremental Few-Shot Learning,
ECCV22(XXXI:205-222).
Springer DOI
2211
BibRef
Li, S.[Shuo],
Liu, F.[Fang],
Hao, Z.[Zehua],
Zhao, K.[Kaibo],
Jiao, L.C.[Li-Cheng],
Unsupervised Few-Shot Image Classification by Learning Features into
Clustering Space,
ECCV22(XXXI:420-436).
Springer DOI
2211
BibRef
Rhee, H.C.[Ho-Chang],
Cho, N.I.[Nam Ik],
Episode Difficulty Based Sampling Method for Few-Shot Classification,
ICIP22(296-300)
IEEE DOI
2211
Training, Codes, Benchmark testing, Sampling methods,
Few-shot Learning, Episodic Training
BibRef
Zarei, M.R.[Mohammad Reza],
Komeili, M.[Majid],
Interpretable Concept-Based Prototypical Networks for Few-Shot
Learning,
ICIP22(4078-4082)
IEEE DOI
2211
Annotations, Machine learning, Extraterrestrial measurements,
Multitasking, Birds, Task analysis, Interpretability, Few-shot, Concept
BibRef
Shirekar, O.K.[Ojas Kishore],
Jamali-Rad, H.[Hadi],
Self-Supervised Class-Cognizant Few-Shot Classification,
ICIP22(976-980)
IEEE DOI
2211
Human intelligence, Dark matter, Benchmark testing,
Iterative methods, Task analysis, Unsupervised learning, contrastive learning
BibRef
Fu, M.H.[Ming-Hao],
Cao, Y.H.[Yun-Hao],
Wu, J.X.[Jian-Xin],
Worst Case Matters for Few-Shot Recognition,
ECCV22(XX:99-115).
Springer DOI
2211
BibRef
Yi, K.[Kai],
Shen, X.Q.[Xiao-Qian],
Gou, Y.H.[Yun-Hao],
Elhoseiny, M.[Mohamed],
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot
Image Classification,
ECCV22(XX:116-132).
Springer DOI
2211
BibRef
Lai, J.X.[Jin-Xiang],
Yang, S.[Siqian],
Liu, W.L.[Wen-Long],
Zeng, Y.[Yi],
Huang, Z.Y.[Zhong-Yi],
Wu, W.L.[Wen-Long],
Liu, J.[Jun],
Gao, B.B.[Bin-Bin],
Wang, C.J.[Cheng-Jie],
tSF: Transformer-Based Semantic Filter for Few-Shot Learning,
ECCV22(XX:1-19).
Springer DOI
2211
BibRef
Hu, Y.[Yanxu],
Ma, A.J.[Andy J.],
Adversarial Feature Augmentation for Cross-domain Few-Shot
Classification,
ECCV22(XX:20-37).
Springer DOI
2211
BibRef
Ma, R.K.[Rong-Kai],
Fang, P.F.[Peng-Fei],
Avraham, G.[Gil],
Zuo, Y.[Yan],
Zhu, T.Y.[Tian-Yu],
Drummond, T.[Tom],
Harandi, M.[Mehrtash],
Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning,
ECCV22(XX:257-274).
Springer DOI
2211
BibRef
Comer, J.F.[Joseph F.],
Jacobson, P.L.[Philip L.],
Hoffmann, H.[Heiko],
Few-Shot Image Classification Along Sparse Graphs,
L3D-IVU22(4186-4194)
IEEE DOI
2210
Target tracking, Limiting, Shape, Training data, Streaming media,
Pattern recognition, Reliability
BibRef
Ye, M.[Meng],
Lin, X.[Xiao],
Burachas, G.[Giedrius],
Divakaran, A.[Ajay],
Yao, Y.[Yi],
Hybrid Consistency Training with Prototype Adaptation for Few-Shot
Learning,
ECV22(2725-2734)
IEEE DOI
2210
Training, Representation learning, Measurement, Interpolation,
Prototypes, Inference algorithms, Pattern recognition
BibRef
Liu, Y.[Yang],
Zhang, W.F.[Wei-Feng],
Xiang, C.[Chao],
Zheng, T.[Tu],
Cai, D.[Deng],
He, X.F.[Xiao-Fei],
Learning to Affiliate: Mutual Centralized Learning for Few-shot
Classification,
CVPR22(14391-14400)
IEEE DOI
2210
Atmospheric measurements, Markov processes,
Particle measurements, Pattern recognition, Task analysis,
Self- semi- meta- unsupervised learning
BibRef
Lee, S.B.[Su-Been],
Moon, W.J.[Won-Jun],
Heo, J.P.[Jae-Pil],
Task Discrepancy Maximization for Fine-grained Few-Shot
Classification,
CVPR22(5321-5330)
IEEE DOI
2210
Quadrature amplitude modulation, Focusing, Benchmark testing,
Time division multiplexing, Encoding, Pattern recognition,
Recognition: detection
BibRef
Chikontwe, P.[Philip],
Kim, S.[Soopil],
Park, S.H.[Sang Hyun],
CAD: Co-Adapting Discriminative Features for Improved Few-Shot
Classification,
CVPR22(14534-14543)
IEEE DOI
2210
Training, Solid modeling, Head, Transfer learning, Prototypes,
Feature extraction, Self- semi- meta- Recognition: detection,
Representation learning
BibRef
Ling, J.[Jie],
Liao, L.[Lei],
Yang, M.[Meng],
Shuai, J.[Jia],
Semi-Supervised Few-shot Learning via Multi-Factor Clustering,
CVPR22(14544-14553)
IEEE DOI
2210
Manifolds, Learning systems, Codes, Fuses, Collaboration,
Benchmark testing, Self- semi- meta- Recognition: detection, retrieval
BibRef
Kang, D.[Dahyun],
Cho, M.[Minsu],
Integrative Few-Shot Learning for Classification and Segmentation,
CVPR22(9969-9980)
IEEE DOI
2210
Image segmentation, Correlation, Computer network reliability,
Semantics, Benchmark testing, Pattern recognition,
Transfer/low-shot/long-tail learning
BibRef
Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
Generating Representative Samples for Few-Shot Classification,
CVPR22(8993-9003)
IEEE DOI
2210
Training, Visualization, Codes, Semantics, Pattern recognition,
Transfer/low-shot/long-tail learning, Statistical methods
BibRef
Liang, K.J.[Kevin J.],
Rangrej, S.B.[Samrudhdhi B.],
Petrovic, V.[Vladan],
Hassner, T.[Tal],
Few-shot Learning with Noisy Labels,
CVPR22(9079-9088)
IEEE DOI
2210
Training, Computational modeling, Prototypes, Transformers,
Robustness, Pattern recognition, Transfer/low-shot/long-tail learning
BibRef
Zhao, R.J.[Rui-Jing],
Zhu, K.[Kai],
Cao, Y.[Yang],
Zha, Z.J.[Zheng-Jun],
AS-Net:
Class-Aware Assistance and Suppression Network for Few-Shot Learning,
MMMod22(II:27-39).
Springer DOI
2203
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.C.[Han-Chen],
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
Li, W.H.[Wei-Hong],
Liu, X.L.[Xia-Lei],
Bilen, H.[Hakan],
Cross-domain Few-shot Learning with Task-specific Adapters,
CVPR22(7151-7160)
IEEE DOI
2210
BibRef
Earlier:
Universal Representation Learning from Multiple Domains for Few-shot
Classification,
ICCV21(9506-9515)
IEEE DOI
2203
Training, Analytical models, Systematics, Costs,
Computational modeling, Estimation, retrieval.
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
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
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.T.[Xue-Ting],
Meng, D.B.[De-Bin],
Gouk, H.[Henry],
Hospedales, T.M.[Timothy M.],
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
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
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
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
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,
Benchmark testing, Calibration, Transfer,
Learning and Optimization
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
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
Tang, S.X.[Shi-Xiang],
Chen, D.P.[Da-Peng],
Bai, L.[Lei],
Liu, K.J.[Kai-Jian],
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
Chen, C.F.[Chao-Fan],
Yang, X.S.[Xiao-Shan],
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,
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.Y.[Zheng-Yu],
Ge, J.X.[Ji-Xie],
Zhan, H.[Heshen],
Huang, S.[Siteng],
Wang, D.L.[Dong-Lin],
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.T.[Zi-Tian],
Maji, S.[Subhransu],
Learned-Miller, E.G.[Erik G.],
Shot in the Dark: Few-Shot Learning with No Base-Class Labels,
LLID21(2662-2671)
IEEE DOI
2109
Supervised learning, Robustness, Pattern recognition
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
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,
Visual systems
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
Lifchitz, Y.[Yann],
Avrithis, Y.[Yannis],
Picard, S.[Sylvaine],
Few-Shot Few-Shot Learning and the role of Spatial Attention,
ICPR21(2693-2700)
IEEE DOI
2105
Training, Focusing, Benchmark testing, Pattern recognition,
Task analysis, Clutter, Standards
BibRef
Nguyen, K.[Khoi],
Todorovic, S.[Sinisa],
A Self-supervised GAN for Unsupervised Few-shot Object Recognition,
ICPR21(3225-3231)
IEEE DOI
2105
Training, Image coding, Performance gain, Probabilistic logic,
Pattern recognition, Object recognition, Image reconstruction
BibRef
Sun, J.[Jiamei],
Lapuschkin, S.[Sebastian],
Samek, W.[Wojciech],
Zhao, Y.Q.[Yun-Qing],
Cheung, N.M.[Ngai-Man],
Binder, A.[Alexander],
Explanation-Guided Training for Cross-Domain Few-Shot Classification,
ICPR21(7609-7616)
IEEE DOI
2105
Training, Heating systems, Visualization, Computational modeling,
Predictive models, Power capacitors, Pattern recognition
BibRef
Hu, Y.Q.[Yu-Qing],
Gripon, V.[Vincent],
Pateux, S.[Stéphane],
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot
Classification,
ICPR21(8164-8171)
IEEE DOI
2105
Interpolation, Feature extraction, Graph neural networks,
Pattern recognition, Standards, Logistics
BibRef
Yan, B.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
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
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.S.[Rogerio S.],
A Broader Study of Cross-domain Few-shot Learning,
ECCV20(XXVII:124-141).
Springer DOI
2011
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
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
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
Lichtenstein, M.[Moshe],
Sattigeri, P.[Prasanna],
Feris, R.S.[Rogerio S.],
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.L.[Jin-Lu],
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.T.[Yu-Tong],
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
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, 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
Li, A.X.[Ao-Xue],
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
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,
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
Zhu, H.[Hao],
Koniusz, P.[Piotr],
EASE: Unsupervised Discriminant Subspace Learning for Transductive
Few-Shot Learning,
CVPR22(9068-9078)
IEEE DOI
2210
Learning systems, Codes, Benchmark testing, Data structures,
Pattern recognition, Standards,
Machine learning
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
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
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
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
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.S.[Rogerio S.],
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.W.[Li-Wei],
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],
Cermelli, F.[Fabio],
Xian, Y.Q.[Yong-Qin],
Mancini, M.[Massimiliano],
Akata, Z.[Zeynep],
Caputo, B.[Barbara],
A Closer Look at Self-training for Zero-Label Semantic Segmentation,
LLID21(2687-2696)
IEEE DOI
2109
Training, Image segmentation, Semantics, Pipelines,
Predictive models, Information filters, Pattern recognition
BibRef
Xian, Y.Q.[Yong-Qin],
Choudhury, S.[Subhabrata],
He, Y.[Yang],
Schiele, B.[Bernt],
Akata, Z.[Zeynep],
Semantic Projection Network for Zero- and Few-Label Semantic
Segmentation,
CVPR19(8248-8257).
IEEE DOI
2002
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
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.J.[Yu-Jia],
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.M.[Zheng-Ming],
Liu, H.F.[Hong-Fu],
Marginalized Latent Semantic Encoder for Zero-Shot Learning,
CVPR19(6184-6192).
IEEE DOI
2002
BibRef
Li, J.[Jin],
Lan, X.G.[Xu-Guang],
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.K.[Peng-Kai],
Wang, H.X.[Han-Xiao],
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.H.[Chang-Hu],
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
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
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
Qi, H.,
Brown, M.,
Lowe, D.G.,
Low-Shot Learning with Imprinted Weights,
CVPR18(5822-5830)
IEEE DOI
1812
Training, Neural networks, Semantics, Google, Training data, Euclidean distance
BibRef
Hariharan, B.[Bharath],
Girshick, R.[Ross],
Low-Shot Visual Recognition by Shrinking and Hallucinating Features,
ICCV17(3037-3046)
IEEE DOI
1802
Recognize categories from very few examples.
image recognition, learning (artificial intelligence),
object recognition, feature hallucination, feature shrinking,
Visualization
BibRef
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Few Shot Learning .