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Zhao, L.[Linglan],
Fang, X.Z.[Xiang-Zhong],
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IVC(110), 2021, pp. 104164.
Elsevier DOI
2106
Few-shot image recognition, Domain adaptation,
Few-shot learning, Transfer learning
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
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
Ji, F.F.[Fan-Fan],
Yuan, X.T.[Xiao-Tong],
Liu, Q.S.[Qing-Shan],
Soft Weight Pruning for Cross-Domain Few-Shot Learning With Unlabeled
Target Data,
MultMed(26), 2024, pp. 6759-6769.
IEEE DOI
2404
Feature extraction, Task analysis, Self-supervised learning,
Training, Data models, Data mining, Deep learning,
soft weight pruning
BibRef
Zhou, F.[Fei],
Wang, P.[Peng],
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Meta-Collaborative Comparison for Effective Cross-Domain Few-Shot
Learning,
PR(156), 2024, pp. 110790.
Elsevier DOI
2408
BibRef
Earlier:
Revisiting Prototypical Network for Cross Domain Few-Shot Learning,
CVPR23(20061-20070)
IEEE DOI
2309
Cross-domain few-shot learning, Meta-learning, Deep neural network
BibRef
Tang, H.J.[Hao-Jin],
Yang, X.F.[Xiao-Fei],
Tang, D.[Dong],
Dong, Y.[Yiru],
Zhang, L.[Li],
Xie, W.X.[Wei-Xin],
Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image
Classification,
RS(16), No. 22, 2024, pp. 4149.
DOI Link
2412
BibRef
Kong, D.C.[De-Chen],
Yang, X.[Xi],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
Perspectives of Calibrated Adaptation for Few-Shot Cross-Domain
Classification,
CirSysVideo(35), No. 3, March 2025, pp. 2410-2421.
IEEE DOI
2503
Feature extraction, Adaptation models, Few shot learning, Context modeling,
Metalearning, Data models, Circuits and systems, feature adaptation
BibRef
Wang, W.[Wei],
Wang, M.Z.[Meng-Zhu],
Huang, C.[Chao],
Wang, C.[Cong],
Mu, J.[Jie],
Nie, F.P.[Fei-Ping],
Cao, X.C.[Xiao-Chun],
Optimal Graph Learning-Based Label Propagation for Cross-Domain Image
Classification,
IP(34), 2025, pp. 1529-1544.
IEEE DOI
2503
Noise, Semisupervised learning, Germanium, Training,
Iterative methods, Image classification, Harmonic analysis,
locally discriminative structure
BibRef
Tang, Y.M.[Yu-Ming],
Peng, Y.X.[Yi-Xing],
Meng, J.[Jingke],
Zheng, W.S.[Wei-Shi],
Rethinking Few-shot Class-incremental Learning: Learning from Yourself,
ECCV24(LXI: 108-128).
Springer DOI
2412
BibRef
Yue, L.[Ling],
Feng, L.[Lin],
Shuai, Q.P.[Qiu-Ping],
Xu, L.X.[Ling-Xiao],
Li, Z.[Zihao],
Diversified Task Augmentation with Redundancy Reduction for
Cross-Domain Few-Shot Learning,
ICIP24(631-637)
IEEE DOI
2411
Training, Metalearning, Adaptation models, Redundancy, Transforms,
Data models, Few-shot learning, meta-learning,
task augmentation
BibRef
Zou, Y.X.[Yi-Xiong],
Liu, Y.C.[Yi-Cong],
Hu, Y.[Yiman],
Li, Y.H.[Yu-Hua],
Li, R.X.[Rui-Xuan],
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning,
CVPR24(23575-23584)
IEEE DOI
2410
Training, Analytical models, Interpolation, Computational modeling,
Training data, Data models
BibRef
Perera, R.[Rashindrie],
Halgamuge, S.[Saman],
Discriminative Sample-Guided and Parameter-Efficient Feature Space
Adaptation for Cross-Domain Few-Shot Learning,
CVPR24(23794-23804)
IEEE DOI Code:
WWW Link.
2410
Training, Adaptation models, Sensitivity, Codes, Few shot learning,
Cross-domain learning, Few-shot learning, Parameter efficiency
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
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
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
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
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
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
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
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
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
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
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
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
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Few Shot Learning .