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
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, 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, Training,
Iterative methods, Image classification, Harmonic analysis,
locally discriminative structure
BibRef
Sun, Y.P.[Yan-Peng],
Chen, Q.[Qiang],
Wang, J.[Jian],
Wang, J.D.[Jing-Dong],
Li, Z.C.[Ze-Chao],
Exploring Effective Factors for Improving Visual In-Context Learning,
IP(34), 2025, pp. 2147-2160.
IEEE DOI Code:
WWW Link.
2504
new task via a few demonstrations.
Visualization, Adaptation models, Computational modeling, Predictive models,
reognition, Semantics, Prompt engineering, prompt fusion
BibRef
Tian, Q.[Qing],
Liu, X.[Xiang],
Zhou, J.Z.[Jia-Zhong],
Zheng, Y.H.[Yu-Hui],
Wan, J.[Jun],
Lei, Z.[Zhen],
Cross-Attention With Conditional Matching for Multi-Target Domain
Adaptation,
CirSysVideo(35), No. 11, November 2025, pp. 10918-10929.
IEEE DOI
2511
Adaptation models, Training, Scalability, Transformers,
Information science, Natural language processing,
condition matching
BibRef
Yao, X.[Xuan],
Peng, X.[Xiao],
Gao, J.Y.[Jun-Yu],
Yuan, Z.Q.[Zhao-Quan],
Wu, X.[Xiao],
Xu, C.S.[Chang-Sheng],
Active Cross-Modal Domain Adaptation,
MultMed(27), 2025, pp. 7974-7987.
IEEE DOI
2511
Active learning, Training, Adaptation models, Annotations, Sports,
Semantics, Labeling, Feature extraction, Artificial intelligence,
cross-modal retrieval
BibRef
Li, Z.K.[Zhao-Kui],
Liu, M.[Ming],
Chen, Y.S.[Yu-Shi],
Xu, Y.M.[Yi-Min],
Li, W.[Wei],
Du, Q.[Qian],
Deep Cross-Domain Few-Shot Learning for Hyperspectral Image
Classification,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI
2112
Training, Task analysis, Feature extraction, Data models,
Deep learning, Hyperspectral imaging, Adaptation models, meta-learning
See also Efficient Deep Learning of Nonlocal Features for Hyperspectral Image Classification.
BibRef
Zhang, Y.X.[Yu-Xiang],
Li, W.[Wei],
Sun, W.D.[Wei-Dong],
Tao, R.[Ran],
Du, Q.[Qian],
Single-Source Domain Expansion Network for Cross-Scene Hyperspectral
Image Classification,
IP(32), 2023, pp. 1498-1512.
IEEE DOI
2303
Training, Generators, Feature extraction, Task analysis, Semantics,
Image classification, Hyperspectral imaging, contrastive learning
BibRef
Li, J.J.[Jiao-Jiao],
Zhang, Z.Y.[Zhi-Yuan],
Song, R.[Rui],
Li, Y.S.[Yun-Song],
Du, Q.[Qian],
SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot
Hyperspectral Image Classification,
IP(33), 2024, pp. 840-855.
IEEE DOI
2402
Task analysis, Data models, Correlation, Transformers,
Feature extraction, Convolution, Indexes, Cross-domain,
HSI classification
BibRef
Li, W.[Wei],
Wu, G.D.[Guo-Dong],
Zhang, F.[Fan],
Du, Q.[Qian],
Hyperspectral Image Classification Using Deep Pixel-Pair Features,
GeoRS(55), No. 2, February 2017, pp. 844-853.
IEEE DOI
1702
hyperspectral imaging
BibRef
Huang, X.Z.[Xi-Zeng],
Dong, Y.[Yanni],
Zhang, Y.X.[Yu-Xiang],
Du, B.[Bo],
Single-Source Frequency Transform for Cross-Scene Classification of
Hyperspectral Image,
IP(34), 2025, pp. 3000-3012.
IEEE DOI
2505
Frequency-domain analysis, Training, Data models,
Frequency diversity, Transforms, Reliability, data manipulation
BibRef
Huang, Y.[Yi],
Peng, J.T.[Jiang-Tao],
Chen, N.[Na],
Sun, W.W.[Wei-Wei],
Du, Q.[Qian],
Ren, K.[Kai],
Huang, K.[Ke],
Cross-scene wetland mapping on hyperspectral remote sensing images
using adversarial domain adaptation network,
PandRS(203), 2023, pp. 37-54.
Elsevier DOI
2310
Wetland mapping, Hyperspectral image, Cross-scene,
Domain adaptation, Adversarial network
BibRef
Li, J.J.[Jiao-Jiao],
Zhang, Z.Y.[Zhi-Yuan],
Song, R.[Rui],
Xu, H.T.[Hai-Tao],
Li, Y.S.[Yun-Song],
Du, Q.[Qian],
Contrastive MLP Network Based on Adjacent Coordinates for
Cross-Domain Zero-Shot Hyperspectral Image Classification,
CirSysVideo(35), No. 8, August 2025, pp. 8377-8390.
IEEE DOI
2508
Hyperspectral imaging, Feature extraction, Contrastive learning,
Zero shot learning, Training, Measurement, Image classification,
HSI classification
BibRef
Feng, S.[Shou],
Zhang, J.[Jinghe],
Fan, Y.Z.[Yuan-Ze],
Liu, X.Y.[Xin-Yao],
Zhao, C.H.[Chun-Hui],
Li, W.[Wei],
Tao, R.[Ran],
Cross-Domain Few-Shot Learning Method Based on Fractional Domain
Information for Hyperspectral Image Multi-Class Change Detection,
CirSysVideo(35), No. 12, December 2025, pp. 12680-12691.
IEEE DOI
2512
Feature extraction, Semantics, Land surface, Hyperspectral imaging,
Few shot learning, Training, Data mining, Optical imaging
BibRef
Zhang, Y.X.[Yu-Xiang],
Li, W.[Wei],
Jia, W.[Wen],
Zhang, M.M.[Meng-Meng],
Tao, R.[Ran],
Liang, S.L.[Shun-Lin],
Cross-Domain Hyperspectral Image Classification Based on
Bi-Directional Domain Adaptation,
CirSysVideo(35), No. 12, December 2025, pp. 12038-12051.
IEEE DOI Code:
WWW Link.
2512
Feature extraction, Bidirectional control, Semantics, Correlation,
Training, Hyperspectral imaging, Transformers, Noise,
transformer
BibRef
Chen, H.J.[Hong-Jie],
Lu, P.[Pei],
Liu, X.Y.[Xiao-Yong],
Ling, Y.[Yuan],
Channel scaling: An efficient feature representation to enhance the
generalization of few-shot learning,
PRL(199), 2026, pp. 163-169.
Elsevier DOI
2512
Few-shot classification, Metric-learning, Channel scaling, Attention mechanism
BibRef
Xiao, K.[Kangyu],
Wang, Z.[Zilei],
Li, J.J.[Jun-Jie],
Semantic-guided Robustness Tuning for Few-shot Transfer Across Extreme
Domain Shift,
ECCV24(XLIX: 303-320).
Springer DOI
2412
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
Hu, Y.X.[Yan-Xu],
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 .