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
Deng, F.Q.[Fu-Qin],
Zhong, J.M.[Jia-Ming],
Li, N.N.[Nan-Nan],
Fu, L.H.[Lan-Hui],
Wang, D.[Dong],
Lam, T.L.[Tin Lun],
Exploring Cross-Video Matching for Few-Shot Video Classification via
Dual-Hierarchy Graph Neural Network Learning,
IVC(139), 2023, pp. 104822.
Elsevier DOI Code:
WWW Link.
2311
Video classification, Few-shot learning, Hierarchy graph neural network
BibRef
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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
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
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.S.[Rogerio S.],
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
Zimmer, L.[Lucas],
Lindauer, M.[Marius],
Hutter, F.[Frank],
Auto-Pytorch:
Multi-Fidelity MetaLearning for Efficient and Robust AutoDL,
PAMI(43), No. 9, September 2021, pp. 3079-3090.
IEEE DOI
2108
Optimization, Open area test sites, Training,
Benchmark testing, Task analysis, Pipelines,
meta-learning
BibRef
Hu, Z.P.[Zheng-Ping],
Li, Z.J.[Zi-Jun],
Wang, X.Y.[Xue-Yu],
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
Cui, Y.W.[Ya-Wen],
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
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
Li, F.[Feimo],
Li, S.B.[Shuai-Bo],
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
Zhou, F.[Fei],
Zhang, L.[Lei],
Wei, W.[Wei],
Meta-Generating Deep Attentive Metric for Few-Shot Classification,
CirSysVideo(32), No. 10, October 2022, pp. 6863-6873.
IEEE DOI
2210
Measurement, Task analysis, Training, Gaussian distribution,
Optimization, Standards, Feature extraction, Few-shot learning, meta-learning
BibRef
Guo, T.[Ting],
Liang, J.Q.[Jian-Qing],
Liang, J.[Jiye],
Xie, G.S.[Guo-Sen],
Cross-modal propagation network for generalized zero-shot learning,
PRL(159), 2022, pp. 125-131.
Elsevier DOI
2206
Zero-shot learning, Generative adversarial network,
Meta-learning, Label propagation
BibRef
Zhang, J.[Ji],
Song, J.K.[Jing-Kuan],
Gao, L.[Lianli],
Liu, Y.[Ye],
Shen, H.T.[Heng Tao],
Progressive Meta-Learning With Curriculum,
CirSysVideo(32), No. 9, September 2022, pp. 5916-5930.
IEEE DOI
2209
Task analysis, Training, Adaptation models, Computational modeling,
Ear, Standards, Pediatrics, Few-shot learning, meta-learning,
hard task-sampling
BibRef
Hu, Z.[Ziye],
Li, W.[Wei],
Gan, Z.X.[Zhong-Xue],
Guo, W.[Weikun],
Zhu, J.[Jiwei],
Wen, J.Z.Q.[James Zhi-Qing],
Zhou, D.[Decheng],
Learning From Visual Demonstrations via Replayed Task-Contrastive
Model-Agnostic Meta-Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8756-8767.
IEEE DOI
2212
Robots, Microstrip, Visualization, Adaptation models, Training data,
Reinforcement learning, Meta-learning, learning to learn
BibRef
Bing, Z.S.[Zhen-Shan],
Lerch, D.[David],
Huang, K.[Kai],
Knoll, A.[Alois],
Meta-Reinforcement Learning in Non-Stationary and Dynamic
Environments,
PAMI(45), No. 3, March 2023, pp. 3476-3491.
IEEE DOI
2302
Task analysis, Training, Robots, Adaptation models, Multitasking,
Inference algorithms, Gaussian mixture model, robotic control
BibRef
Martins, V.E.[Vinicius Eiji],
Cano, A.[Alberto],
Barbon Junior, S.[Sylvio],
Meta-learning for dynamic tuning of active learning on stream
classification,
PR(138), 2023, pp. 109359.
Elsevier DOI
2303
Meta-learning, Active learning, Data stream, Concept drift
BibRef
Li, Y.[Yun],
Liu, Z.[Zhe],
Yao, L.[Lina],
Chang, X.J.[Xiao-Jun],
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning,
MultMed(25), 2023, pp. 1600-1610.
IEEE DOI
2306
Task analysis, Modulation, Adaptation models, Visualization, Training,
Generators, Semantics, Zero-shot learning, meta-learning, image retrieval
BibRef
Jiang, S.Q.[Shu-Qiang],
Zhu, Y.[Yaohui],
Liu, C.L.[Chen-Long],
Song, X.H.[Xin-Hang],
Li, X.Y.[Xiang-Yang],
Min, W.Q.[Wei-Qing],
Dataset Bias in Few-Shot Image Recognition,
PAMI(45), No. 1, January 2023, pp. 229-246.
IEEE DOI
2212
Task analysis, Visualization, Learning systems, Adaptation models,
Image recognition, Training, Complexity theory, Dataset bias, meta-learning
BibRef
Zhang, L.[Lei],
Zhou, F.[Fei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Meta-hallucinating prototype for few-shot learning promotion,
PR(136), 2023, pp. 109235.
Elsevier DOI
2301
Few-shot learning, Prototype hallucination, Meta-learning
BibRef
Cheng, J.[Jun],
Hao, F.[Fusheng],
He, F.X.[Feng-Xiang],
Liu, L.[Liu],
Zhang, Q.[Qieshi],
Mixer-Based Semantic Spread for Few-Shot Learning,
MultMed(25), 2023, pp. 191-202.
IEEE DOI
2301
Semantics, Feature extraction, Training, Mixers, Task analysis,
Visualization, Few-shot learning, metric learning-based meta-learning
BibRef
Ye, H.J.[Han-Jia],
Han, L.[Lu],
Zhan, D.C.[De-Chuan],
Revisiting Unsupervised Meta-Learning via the Characteristics of
Few-Shot Tasks,
PAMI(45), No. 3, March 2023, pp. 3721-3737.
IEEE DOI
2302
Task analysis, Unified modeling language, Training,
Feature extraction, Semantics, Labeling, Visualization,
self-supervised learning
BibRef
Tabealhojeh, H.[Hadi],
Adibi, P.[Peyman],
Karshenas, H.[Hossein],
Roy, S.K.[Soumava Kumar],
Harandi, M.[Mehrtash],
RMAML: Riemannian meta-learning with orthogonality constraints,
PR(140), 2023, pp. 109563.
Elsevier DOI
2305
Meta-learning, Geometry-aware optimization,
Riemannian manifolds, Few-shot image classification
BibRef
Zhao, Y.Q.[Yun-Qing],
Cheung, N.M.[Ngai-Man],
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot
Classification,
IP(32), 2023, pp. 2252-2266.
IEEE DOI
2305
Training, Power capacitors, Task analysis, Data models,
Knowledge engineering, Adaptation models, Training data, meta-learning
BibRef
Zhang, B.Q.[Bao-Quan],
Jiang, H.[Hao],
Li, X.[Xutao],
Feng, S.S.[Shan-Shan],
Ye, Y.M.[Yun-Ming],
Luo, C.[Chen],
Ye, R.[Rui],
MetaDT: Meta Decision Tree With Class Hierarchy for Interpretable
Few-Shot Learning,
CirSysVideo(33), No. 6, June 2023, pp. 2826-2838.
IEEE DOI
2306
Decision trees, Visualization, Dogs, Task analysis, Semantics,
Neural networks, Heating systems, Few-shot learning, meta-learning,
class hierarchy
BibRef
Peng, D.[Danni],
Pan, S.J.L.[Sinno Jia-Lin],
Clustered Task-Aware Meta-Learning by Learning from Learning Paths,
PAMI(45), No. 8, August 2023, pp. 9426-9438.
IEEE DOI
2307
WWW Link. Task analysis, Training, Feature extraction, Modulation, Trajectory,
Optimization, Knowledge engineering, Task clustering,
task-aware meta-learning
BibRef
Gao, Z.[Zhi],
Wu, Y.W.[Yu-Wei],
Harandi, M.[Mehrtash],
Jia, Y.D.[Yun-De],
Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data,
PAMI(45), No. 2, February 2023, pp. 1545-1562.
IEEE DOI
2301
Manifolds, Task analysis, Optimization, Neural networks,
Adaptation models, Geometry, Data models, Meta-learning, curvature
BibRef
Nguyen, C.[Cuong],
Do, T.T.[Thanh-Toan],
Carneiro, G.[Gustavo],
PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors,
PAMI(45), No. 1, January 2023, pp. 841-851.
IEEE DOI
2212
Task analysis, Data models, Training, Adaptation models,
Optimization, Predictive models, Gaussian distribution, PAC bayes,
transfer learning
BibRef
Baik, S.[Sungyong],
Choi, M.[Myungsub],
Choi, J.[Janghoon],
Kim, H.[Heewon],
Lee, K.M.[Kyoung Mu],
Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning,
PAMI(46), No. 3, March 2024, pp. 1441-1454.
IEEE DOI
2402
Task analysis, Optimization, Mathematical models,
Adaptation models, Visualization, Training, Neural networks,
visual tracking
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
Wang, R.[Ruohan],
Falk, J.I.T.[John Isak Texas],
Pontil, M.[Massimiliano],
Ciliberto, C.[Carlo],
Robust Meta-Representation Learning via Global Label Inference and
Classification,
PAMI(46), No. 4, April 2024, pp. 1996-2010.
IEEE DOI
2403
Task analysis, Metalearning, Training, Standards, Feature extraction,
Adaptation models, Merging, Few-Shot image classification,
representation learning
BibRef
Wang, C.[Chengkun],
Zheng, W.Z.[Wen-Zhao],
Zhu, Z.[Zheng],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Introspective Deep Metric Learning,
PAMI(46), No. 4, April 2024, pp. 1964-1980.
IEEE DOI
2403
Measurement, Uncertainty, Semantics, Training,
Measurement uncertainty, Probabilistic logic, Image retrieval,
uncertainty-aware similarity judgments
BibRef
Yan, X.G.[Xin-Gao],
Shao, F.[Feng],
Chen, H.W.[Hang-Wei],
Jiang, Q.P.[Qiu-Ping],
Hybrid CNN-transformer based meta-learning approach for personalized
image aesthetics assessment,
JVCIR(98), 2024, pp. 104044.
Elsevier DOI
2402
Meta-Learning, Personalized image aesthetics assessment
BibRef
Zhang, W.[Wei],
Wang, X.S.[Xue-Song],
Wang, H.Y.[Hao-Yu],
Cheng, Y.[Yuhu],
Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data
Classification,
RS(16), No. 6, 2024, pp. 1055.
DOI Link
2403
BibRef
Wang, Y.[Yi],
Huang, C.Q.[Chang-Qin],
Li, M.[Ming],
Huang, Q.[Qionghao],
Wu, X.M.[Xue-Mei],
Wu, J.[Jia],
AG-Meta: Adaptive graph meta-learning via representation consistency
over local subgraphs,
PR(151), 2024, pp. 110387.
Elsevier DOI
2404
Embedding representation, Local subgraphs,
Few-shot graph learning, Meta-learning
BibRef
Wang, L.Z.[Lian-Zhe],
Zhou, S.[Shiji],
Zhang, S.H.[Shang-Hang],
Chu, X.[Xu],
Chang, H.[Heng],
Zhu, W.W.[Wen-Wu],
Improving Generalization of Meta-Learning with Inverted
Regularization at Inner-Level,
CVPR23(7826-7835)
IEEE DOI
2309
BibRef
Hu, Z.X.[Zi-Xuan],
Shen, L.[Li],
Wang, Z.[Zhenyi],
Liu, T.L.[Tong-Liang],
Yuan, C.[Chun],
Tao, D.C.[Da-Cheng],
Architecture, Dataset and Model-Scale Agnostic Data-free
Meta-Learning,
CVPR23(7736-7745)
IEEE DOI
2309
BibRef
Subramanyam, R.[Rakshith],
Heimann, M.[Mark],
Jayram, T.S.,
Anirudh, R.[Rushil],
Thiagarajan, J.J.[Jayaraman J.],
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot
Classification,
WACV23(2478-2486)
IEEE DOI
2302
Aggregates, Semantics, Prototypes, Modulation, Benchmark testing,
Encoding, Algorithms: Machine learning architectures, visual reasoning
BibRef
Qin, X.R.[Xiao-Rong],
Song, X.H.[Xin-Hang],
Jiang, S.Q.[Shu-Qiang],
Bi-Level Meta-Learning for Few-Shot Domain Generalization,
CVPR23(15900-15910)
IEEE DOI
2309
BibRef
Kamenou, E.[Eleni],
del Rincón, J.M.[Jesús Martínez],
Miller, P.[Paul],
Devlin-Hill, P.[Patricia],
A Meta-learning Approach for Domain Generalisation across Visual
Modalities in Vehicle Re-identification,
PBVS23(385-393)
IEEE DOI
2309
BibRef
Kang, S.[Suhyun],
Hwang, D.[Duhun],
Eo, M.[Moonjung],
Kim, T.[Taesup],
Rhee, W.[Wonjong],
Meta-Learning with a Geometry-Adaptive Preconditioner,
CVPR23(16080-16090)
IEEE DOI
2309
BibRef
Ragonesi, R.[Ruggero],
Morerio, P.[Pietro],
Murino, V.[Vittorio],
Learning unbiased classifiers from biased data with meta-learning,
FaDE-TCV23(1-9)
IEEE DOI
2309
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
Pan, X.H.[Xiao-Hang],
Li, F.[Fanzhang],
Class-wise Attention Reinforcement for Semi-supervised Meta-Learning,
ICPR22(4479-4485)
IEEE DOI
2212
Prototypes, Benchmark testing, Task analysis
BibRef
Domoguen, J.K.L.[Jansen Keith L.],
Naval, P.C.[Prospero C.],
Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot
Learning,
ICPR22(4927-4933)
IEEE DOI
2212
Deep learning, Adaptation models, Particle separators, Prototypes,
Benchmark testing, Task analysis
BibRef
Volpi, R.[Riccardo],
Larlus, D.[Diane],
Rogez, G.[Grégory],
Continual Adaptation of Visual Representations via Domain
Randomization and Meta-learning,
CVPR21(4441-4451)
IEEE DOI
2111
Visualization, Adaptation models, Image segmentation,
Computational modeling, Semantics, Pattern recognition
BibRef
Cheng, Y.C.[Yuan-Chia],
Lin, C.S.[Ci-Siang],
Yang, F.E.[Fu-En],
Wang, Y.C.A.F.[Yu-Chi-Ang 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
Chen, Y.B.[Yin-Bo],
Liu, Z.A.[Zhu-Ang],
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
Algan, G.[Görkem],
Ulusoy, I.[Ilkay],
Meta Soft Label Generation for Noisy Labels,
ICPR21(7142-7148)
IEEE DOI
2105
Training, Degradation, Adaptation models, Neural networks, Clothing,
Training data, deep learning, label noise, meta-learning
BibRef
Kim, J.[Jin],
Lee, J.Y.[Ji-Young],
Park, J.[Jungin],
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Self-Balanced Learning for Domain Generalization,
ICIP21(779-783)
IEEE DOI
2201
Training, Degradation, Adaptive systems, Image processing,
Training data, Predictive models, Domain generalization, meta-learning
BibRef
Jamal, M.A.[Muhammad Abdullah],
Wang, L.Q.[Li-Qiang],
Gong, B.Q.[Bo-Qing],
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning,
ICCV21(6557-6566)
IEEE DOI
2203
Computational modeling, Space exploration, Object recognition,
Task analysis, Optimization and learning methods,
Efficient training and inference methods
BibRef
Shu, Y.[Yang],
Cao, Z.J.[Zhang-Jie],
Wang, C.Y.[Chen-Yu],
Wang, J.M.[Jian-Min],
Long, M.S.[Ming-Sheng],
Open Domain Generalization with Domain-Augmented Meta-Learning,
CVPR21(9619-9628)
IEEE DOI
2111
Computational modeling, Data models,
Pattern recognition, Microstrip, Task analysis
BibRef
Liu, B.,
Kang, H.,
Li, H.,
Hua, G.,
Vasconcelos, N.M.,
Few-Shot Open-Set Recognition Using Meta-Learning,
CVPR20(8795-8804)
IEEE DOI
2008
Training, Measurement, Task analysis, Robustness, Entropy,
Image recognition, Face recognition
BibRef
Rajasegaran, J.,
Khan, S.,
Hayat, M.,
Khan, F.S.,
Shah, M.,
iTAML: An Incremental Task-Agnostic Meta-learning Approach,
CVPR20(13585-13594)
IEEE DOI
2008
Task analysis, Adaptation models, Training, Stability analysis,
Interference, Predictive models, Heuristic algorithms
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
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
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, 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
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, Training, Neural networks,
Adaptation models, Standards, Machine learning
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
Li, D.[Da],
Hospedales, T.M.[Timothy M.],
Online Meta-learning for Multi-source and Semi-supervised Domain
Adaptation,
ECCV20(XVI: 382-403).
Springer DOI
2010
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Simon, C.[Christian],
Koniusz, P.[Piotr],
Nock, R.[Richard],
Harandi, M.[Mehrtash],
On Modulating the Gradient for Meta-learning,
ECCV20(VIII:556-572).
Springer DOI
2011
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Jamal, M.A.[Muhammad Abdullah],
Qi, G.J.[Guo-Jun],
Task Agnostic Meta-Learning for Few-Shot Learning,
CVPR19(11711-11719).
IEEE DOI
2002
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Achille, A.[Alessandro],
Lam, M.[Michael],
Tewari, R.[Rahul],
Ravichandran, A.[Avinash],
Maji, S.[Subhransu],
Fowlkes, C.[Charless],
Soatto, S.[Stefano],
Perona, P.[Pietro],
Task2Vec: Task Embedding for Meta-Learning,
ICCV19(6429-6438)
IEEE DOI
2004
Vectorial representations of visual classification tasks which can be
used to reason about the nature of those tasks.
feature extraction, image classification,
learning (artificial intelligence), vectorial representations,
BibRef
Ding, Y.M.[Yue-Ming],
Tian, X.[Xia],
Yin, L.R.[Li-Rong],
Chen, X.B.[Xia-Bing],
Liu, S.[Shan],
Yang, B.[Bo],
Zheng, W.F.[Wen-Feng],
Multi-scale Relation Network for Few-shot Learning Based on
Meta-learning,
CVS19(343-352).
Springer DOI
1912
BibRef
Herath, S.[Samitha],
Harandi, M.[Mehrtash],
Fernando, B.[Basura],
Nock, R.[Richard],
Min-Max Statistical Alignment for Transfer Learning,
CVPR19(9280-9289).
IEEE DOI
2002
BibRef
Krijthe, J.H.[Jesse H.],
Ho, T.K.[Tin Kam],
Loog, M.[Marco],
Improving cross-validation based classifier selection using
meta-learning,
ICPR12(2873-2876).
WWW Link.
1302
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Evaluation and Analysis of Learning Techniques .