14.2.7.1.1 Few-Shot Incremental Learning

Ji, Z.[Zhong], Hou, Z.S.[Zhi-Shen], Liu, X.Y.[Xi-Yao], Pang, Y.W.[Yan-Wei], Li, X.L.[Xue-Long],
Memorizing Complementation Network for Few-Shot Class-Incremental Learning,
IP(32), 2023, pp. 937-948.
IEEE DOI 2301
Task analysis, Power capacitors, Ensemble learning, Knowledge engineering, Feature extraction, Adaptation models, memorizing complementation BibRef

Liu, J.R.[Jing-Ren], Ji, Z.[Zhong], Pang, Y.W.[Yan-Wei], Yu, Y.L.[Yun-Long],
NTK-Guided Few-Shot Class Incremental Learning,
IP(33), 2024, pp. 6029-6044.
IEEE DOI 2411
Power capacitors, Convergence, Training, Kernel, Optimization, Neural networks, Metalearning, Incremental learning, self-supervised learning BibRef

Wang, X.[Xuan], Ji, Z.[Zhong], Yu, Y.L.[Yun-Long], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong],
Model Attention Expansion for Few-Shot Class-Incremental Learning,
IP(33), 2024, pp. 4419-4431.
IEEE DOI 2408
Adaptation models, Task analysis, Training, Power capacitors, Feature extraction, Predictive models, Scattering, attention field BibRef

Wang, X.[Xuan], Ji, Z.[Zhong], Liu, X.Y.[Xi-Yao], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong],
On the Approximation Risk of Few-Shot Class-Incremental Learning,
ECCV24(LI: 162-178).
Springer DOI 2412
BibRef

Yang, B.[Boyu], Lin, M.B.[Ming-Bao], Zhang, Y.X.[Yun-Xiao], Liu, B.H.[Bing-Hao], Liang, X.D.[Xiao-Dan], Ji, R.R.[Rong-Rong], Ye, Q.X.[Qi-Xiang],
Dynamic Support Network for Few-Shot Class Incremental Learning,
PAMI(45), No. 3, March 2023, pp. 2945-2951.
IEEE DOI 2302
Power capacitors, Training, Feature extraction, Adaptation models, Task analysis, Generators, Data models, support network BibRef

Liu, B.H.[Bing-Hao], Yang, B.[Boyu], Xie, L.X.[Ling-Xi], Wang, R.[Ren], Tian, Q.[Qi], Ye, Q.X.[Qi-Xiang],
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning,
PAMI(45), No. 10, October 2023, pp. 12699-12706.
IEEE DOI 2310
BibRef

Guo, C.X.[Chen-Xu], Zhao, Q.[Qi], Lyu, S.C.[Shu-Chang], Liu, B.H.[Bing-Hao], Wang, C.L.[Chun-Lei], Chen, L.[Lijiang], Cheng, G.L.[Guang-Liang],
Decision Boundary Optimization for Few-shot Class-Incremental Learning,
VCL23(3493-3503)
IEEE DOI 2401
BibRef

Zhao, H.B.[Han-Bin], Fu, Y.J.[Yong-Jian], Kang, M.T.[Min-Tong], Tian, Q.[Qi], Wu, F.[Fei], Li, X.[Xi],
MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning,
PAMI(46), No. 3, March 2024, pp. 1576-1588.
IEEE DOI 2402
Task analysis, Power capacitors, Knowledge engineering, Training, Frequency-domain analysis, Extraterrestrial measurements, class-incremental learning BibRef

Pan, Z.C.[Zi-Cheng], Zhang, W.C.[Wei-Chuan], Yu, X.H.[Xiao-Han], Zhang, M.[Miaohua], Gao, Y.S.[Yong-Sheng],
Pseudo-set Frequency Refinement architecture for fine-grained few-shot class-incremental learning,
PR(155), 2024, pp. 110686.
Elsevier DOI 2408
Few-shot class-incremental learning, Fine-grained classification, Frequency analysis, Feature space optimization BibRef

Shao, M.W.[Ming-Wen], Zhuang, X.K.[Xin-Kai], Zhang, L.X.[Li-Xu], Zuo, W.M.[Wang-Meng],
Pseudo initialization based Few-Shot Class Incremental Learning,
CVIU(247), 2024, pp. 104067.
Elsevier DOI 2408
Few-Shot Class Incremental Learning, Embedding space, Pseudo initialization BibRef

Xu, W.[Wan], Huang, T.Y.[Tian-Yu], Qu, T.Y.[Tian-Yuan], Yang, G.L.[Guang-Lei], Guo, Y.W.[Yi-Wen], Zuo, W.M.[Wang-Meng],
FILP-3D: Enhancing 3D few-shot class-incremental learning with pre-trained vision-language models,
PR(165), 2025, pp. 111558.
Elsevier DOI Code:
WWW Link. 2505
3D classification, FSCIL, 3D from multi-view, V-L pre-trained model BibRef

Li, J.S.[Jia-Shuo], Dong, S.L.[Song-Lin], Gong, Y.H.[Yi-Hong], He, Y.H.[Yu-Hang], Wei, X.[Xing],
Analogical Learning-Based Few-Shot Class-Incremental Learning,
CirSysVideo(34), No. 7, July 2024, pp. 5493-5504.
IEEE DOI 2407
Training, Power capacitors, Adaptation models, Optimization, Convolutional neural networks, Brain modeling, Task analysis, class classifier constructor BibRef

Tao, X.Y.[Xiao-Yu], Hong, X.P.[Xiao-Peng], Chang, X.Y.[Xin-Yuan], Dong, S.L.[Song-Lin], Wei, X.[Xing], Gong, Y.H.[Yi-Hong],
Few-Shot Class-Incremental Learning,
CVPR20(12180-12189)
IEEE DOI 2008
Power capacitors, Training, Task analysis, Topology, Adaptation models, Neural networks, Network topology BibRef

Zhao, Y.F.[Yi-Fan], Li, J.[Jia], Song, Z.[Zeyin], Tian, Y.H.[Yong-Hong],
Language-Inspired Relation Transfer for Few-Shot Class-Incremental Learning,
PAMI(47), No. 2, February 2025, pp. 1089-1102.
IEEE DOI 2501
Visualization, Training, Power capacitors, Light rail systems, Few shot learning, Contrastive learning, Prototypes, Metalearning, language-inspired relation transfer BibRef

Zhang, J.H.[Jing-Hua], Liu, L.[Li], Silvén, O.[Olli], Pietikäinen, M.[Matti], Hu, D.[Dewen],
Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey,
PAMI(47), No. 4, April 2025, pp. 2924-2945.
IEEE DOI 2503
Power capacitors, Surveys, Training, Systematics, Measurement, Object detection, Data privacy, Data models, image classification BibRef

Hu, K.[Kai], Wang, Y.J.[Yun-Jiang], Zhang, Y.[Yuan], Gao, X.[Xieping],
Progressive Learning Strategy for Few-Shot Class-Incremental Learning,
Cyber(55), No. 3, March 2025, pp. 1210-1223.
IEEE DOI 2503
Data models, Noise, Training, Power capacitors, Robustness, Perturbation methods, Adaptation models, Interference, virtual classes BibRef

Li, S.[Shuo], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Li, L.L.[Ling-Ling], Chen, P.H.[Pu-Hua], Liu, X.[Xu], Ma, W.P.[Wen-Ping],
Prompt-Based Concept Learning for Few-Shot Class-Incremental Learning,
CirSysVideo(35), No. 5, May 2025, pp. 4991-5005.
IEEE DOI 2505
Power capacitors, Adaptation models, Birds, Training data, Stability plasticity, Incremental learning, few-shot class-incremental learning BibRef

Zhang, T.[Tong], Zhao, Y.F.[Yi-Fan], Wang, L.Y.[Liang-Yu], Li, J.[Jia],
Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning,
IJCV(133), No. 8, August 2025, pp. 5118-5137.
Springer DOI 2508
Construct intermediat versions. Multiple dataset used. BibRef

Zhao, L.J.[Li-Jun], Chen, Z.D.[Zhen-Duo], Wang, Y.X.[Yong-Xin], Luo, X.[Xin], Xu, X.S.[Xin-Shun],
Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning,
CVPR25(25657-25666)
IEEE DOI 2508
Adaptation models, Accuracy, Feature extraction, Extraterrestrial measurements, Power capacitors, Optimization, Overfitting BibRef

Lee, J.[Juntae], Hayat, M.[Munawar], Yun, S.[Sungrack],
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning,
CVPR25(15329-15338)
IEEE DOI 2508
Training, Continuing education, Adaptation models, Incremental learning, Feature extraction, Market research, few-shot BibRef

Chen, Y.Y.[Yi-Yang], Ding, T.Y.[Tian-Yu], Wang, L.[Lei], Huo, J.[Jing], Gao, Y.[Yang], Li, W.B.[Wen-Bin],
Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration,
CVPR25(9881-9890)
IEEE DOI Code:
WWW Link. 2508
Measurement, Training, Visualization, Adaptation models, Accuracy, Prototypes, Robustness, Calibration, Power capacitors, few-shot class-incremental learning BibRef

Nema, P.[Parinita], Kurmi, V.K.[Vinod K],
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning,
WACV25(6394-6403)
IEEE DOI Code:
WWW Link. 2505
Training, Representation learning, Incremental learning, Codes, Contrastive learning, Benchmark testing, Vectors, Power capacitors, feature augmentation BibRef

Hu, Y.J.[Yi-Jie], Yang, G.[Guanyu], Tan, Z.R.[Zhao-Rui], Huang, X.W.[Xiao-Wei], Huang, K.[Kaizhu], Wang, Q.F.[Qiu-Feng],
Covariance-Based Space Regularization for Few-Shot Class Incremental Learning,
WACV25(9566-9576)
IEEE DOI 2505
Training, Incremental learning, Perturbation methods, Semantics, Force, Benchmark testing, Data models, Power capacitors, Overfitting BibRef

Deng, Y.[Yao], Xiang, X.[Xiang],
Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning,
WACV24(1956-1965)
IEEE DOI 2404
Prototypes, Benchmark testing, Data models, Power capacitors, Task analysis, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Tan, Y.[Yuwen], Xiang, X.[Xiang],
Cross-Domain Few-Shot Incremental Learning for Point-Cloud Recognition,
WACV24(2296-2305)
IEEE DOI 2404
Adaptation models, Robot sensing systems, Power capacitors, Sensors, Object recognition, Algorithms, Image recognition and understanding BibRef

Kim, S.[Solang], Jeong, Y.[Yuho], Park, J.S.[Joon Sung], Yoon, S.W.[Sung Whan],
MICS: Midpoint Interpolation to Learn Compact and Separated Representations for Few-Shot Class-Incremental Learning,
WACV24(2225-2234)
IEEE DOI Code:
WWW Link. 2404
Training, Microwave integrated circuits, Interpolation, Codes, Computational modeling, Benchmark testing, Algorithms BibRef

Liu, C.X.[Chen-Xi], Wang, Z.[Zhenyi], Xiong, T.Y.[Tian-Yi], Chen, R.[Ruibo], Wu, Y.H.[Yi-Han], Guo, J.F.[Jun-Feng], Huang, H.[Heng],
Few-shot Class Incremental Learning with Attention-aware Self-adaptive Prompt,
ECCV24(LXXXI: 1-18).
Springer DOI 2412
BibRef

Ahmadi, S.[Sahar], Cheraghian, A.[Ali], Saberi, M.[Morteza], Abir, M.T.[Md. Towsif], Dastmalchi, H.[Hamidreza], Hussain, F.[Farookh], Rahman, S.[Shafin],
Foundation Model-powered 3d Few-shot Class Incremental Learning via Training-free Adaptor,
ACCV24(X: 178-195).
Springer DOI 2412
BibRef

Park, K.H.[Keon-Hee], Song, K.[Kyungwoo], Park, G.M.[Gyeong-Moon],
Pre-trained Vision and Language Transformers are Few-Shot Incremental Learners,
CVPR24(23881-23890)
IEEE DOI Code:
WWW Link. 2410
Incremental learning, Codes, Computational modeling, Semantics, Transformers, Power capacitors, Incremental learning, Parameter Efficient Tuning BibRef

d'Alessandro, M.[Marco], Alonso, A.[Alberto], Calabrés, E.[Enrique], Galar, M.[Mikel],
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning,
VCL23(3385-3395)
IEEE DOI 2401
BibRef

Song, Z.[Zeyin], Zhao, Y.F.[Yi-Fan], Shi, Y.J.[Yu-Jun], Peng, P.X.[Pei-Xi], Yuan, L.[Li], Tian, Y.H.[Yong-Hong],
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning,
CVPR23(24183-24192)
IEEE DOI 2309
BibRef

Zhuang, H.P.[Hui-Ping], Weng, Z.Y.[Zhen-Yu], He, R.[Run], Lin, Z.P.[Zhi-Ping], Zeng, Z.Q.[Zi-Qian],
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task,
CVPR23(7746-7755)
IEEE DOI 2309
BibRef

Zhao, L.L.[Ling-Lan], Lu, J.[Jing], Xu, Y.L.[Yun-Lu], Cheng, Z.Z.[Zhan-Zhan], Guo, D.S.[Da-Shan], Niu, Y.[Yi], Fang, X.Z.[Xiang-Zhong],
Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation,
CVPR23(11838-11847)
IEEE DOI 2309
BibRef

Pan, Z.C.[Zi-Cheng], Yu, X.H.[Xiao-Han], Zhang, M.[Miaohua], Gao, Y.S.[Yong-Sheng],
SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning,
WACV23(6264-6273)
IEEE DOI 2302
Knowledge engineering, Visualization, Layout, Self-supervised learning, Benchmark testing, Agriculture BibRef

Kalla, J.[Jayateja], Biswas, S.[Soma],
S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning,
ECCV22(XXV:432-448).
Springer DOI 2211
BibRef

Peng, C.[Can], Zhao, K.[Kun], Wang, T.R.[Tian-Ren], Li, M.[Meng], Lovell, B.C.[Brian C.],
Few-Shot Class-Incremental Learning from an Open-Set Perspective,
ECCV22(XXV:382-397).
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

Lin, G.L.[Guo-Liang], Chu, H.[Hanlu], Lai, H.J.[Han-Jiang],
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector,
CVPR22(89-98)
IEEE DOI 2210
Connectors, Knowledge engineering, Upper bound, Codes, Neural networks, Training data, Machine learning, Transfer/low-shot/long-tail learning BibRef

Joseph, K.J., Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], Balasubramanian, V.N.[Vineeth N],
Energy-based Latent Aligner for Incremental Learning,
CVPR22(7442-7451)
IEEE DOI 2210
Manifolds, Deep learning, Codes, Pipelines, Object detection, Detectors, Recognition: detection, categorization, retrieval, Transfer/low-shot/long-tail learning BibRef

Ahn, H.[Hongjoon], Kwak, J.[Jihwan], Lim, S.B.[Su-Bin], Bang, H.[Hyeonsu], Kim, H.[Hyojun], Moon, T.[Taesup],
SS-IL: Separated Softmax for Incremental Learning,
ICCV21(824-833)
IEEE DOI 2203
Systematics, Training data, Benchmark testing, Task analysis, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Kukleva, A.[Anna], Kuehne, H.[Hilde], Schiele, B.[Bernt],
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting,
ICCV21(9000-9009)
IEEE DOI 2203
Training, Deep learning, Benchmark testing, Entropy, Calibration, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Cheraghian, A.[Ali], Rahman, S.[Shafin], Ramasinghe, S.[Sameera], Fang, P.F.[Peng-Fei], Simon, C.[Christian], Petersson, L.[Lars], Harandi, M.[Mehrtash],
Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces,
ICCV21(8641-8650)
IEEE DOI 2203
Training, Visualization, Adaptation models, Computational modeling, Semantics, Prototypes, BibRef

Zhu, K.[Kai], Cao, Y.[Yang], Zhai, W.[Wei], Cheng, J.[Jie], Zha, Z.J.[Zheng-Jun],
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning,
CVPR21(6797-6806)
IEEE DOI 2111
Adaptation models, Computational modeling, Prototypes, Benchmark testing, Power capacitors BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Continual Learning .