Hu, Q.H.[Qing-Hua],
Gao, Y.C.[Yu-Cong],
Cao, B.[Bing],
Curiosity-Driven Class-Incremental Learning via Adaptive Sample
Selection,
CirSysVideo(32), No. 12, December 2022, pp. 8660-8673.
IEEE DOI
2212
Task analysis, Adaptation models, Knowledge engineering,
Data models, Uncertainty, Training, Computational modeling, novelty
BibRef
Fu, Z.L.[Zhi-Ling],
Wang, Z.[Zhe],
Xu, X.L.[Xin-Lei],
Li, D.D.[Dong-Dong],
Yang, H.[Hai],
Knowledge aggregation networks for class incremental learning,
PR(137), 2023, pp. 109310.
Elsevier DOI
2302
Class incremental learning, Catastrophic forgetting,
Dual-branch network, Knowledge aggregation, Model compression
BibRef
Lin, H.[Huiwei],
Feng, S.S.[Shan-Shan],
Li, X.[Xutao],
Li, W.T.[Wen-Tao],
Ye, Y.M.[Yun-Ming],
Anchor Assisted Experience Replay for Online Class-Incremental
Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2217-2232.
IEEE DOI
2305
Automobiles, Airplanes, Task analysis, Atmospheric modeling,
Training, Reservoirs, Memory management,
image recognition
BibRef
Shi, L.[Lei],
Zhao, K.[Kai],
Fu, Z.Y.[Zhen-Yong],
Boosting separated softmax with discrimination for class incremental
learning,
JVCIR(95), 2023, pp. 103899.
Elsevier DOI
2309
Incremental learning, Discrimination enhancement,
Discriminative separated softmax
BibRef
Wang, S.[Shaokun],
Shi, W.W.[Wei-Wei],
Dong, S.L.[Song-Lin],
Gao, X.Y.[Xin-Yuan],
Song, X.[Xiang],
Gong, Y.H.[Yi-Hong],
Semantic Knowledge Guided Class-Incremental Learning,
CirSysVideo(33), No. 10, October 2023, pp. 5921-5931.
IEEE DOI
2310
BibRef
Wei, K.[Kun],
Yang, X.[Xu],
Xu, Z.[Zhe],
Deng, C.[Cheng],
Class-Incremental Unsupervised Domain Adaptation via Pseudo-Label
Distillation,
IP(33), 2024, pp. 1188-1198.
IEEE DOI
2402
Adaptation models, Training, Feature extraction,
Information filters, Task analysis, Prototypes, Data models,
pseudo-label distillation
BibRef
Yu, J.P.[Jia-Ping],
Yang, M.[Muli],
Wu, A.[Aming],
Deng, C.[Cheng],
Memory-Enhanced Confidence Calibration for Class-Incremental
Unsupervised Domain Adaptation,
MultMed(27), 2025, pp. 610-621.
IEEE DOI
2502
Data models, Training, Adaptation models, Calibration,
Feature extraction, Character recognition, Accuracy, Tail,
causality
BibRef
Ma, B.T.[Bing-Tao],
Cong, Y.[Yang],
Ren, Y.[Yu],
IOSL: Incremental Open Set Learning,
CirSysVideo(34), No. 4, April 2024, pp. 2235-2248.
IEEE DOI
2404
Task analysis, Training, Prototypes, Robots, Adaptation models,
Feature extraction, Extraterrestrial measurements, class incremental learning
BibRef
Wu, R.[Ran],
Liu, H.Y.[Huan-Yu],
Yue, Z.[Zongcheng],
Li, J.B.[Jun-Bao],
Sham, C.W.[Chiu-Wing],
Hyper-feature aggregation and relaxed distillation for class
incremental learning,
PR(152), 2024, pp. 110440.
Elsevier DOI
2405
Class incremental learning, Relaxed knowledge distillation,
Hyper-feature aggregation
BibRef
Wu, R.[Ran],
Liu, H.Y.[Huan-Yu],
Yue, Z.[Zongcheng],
Sham, C.W.[Chiu-Wing],
Li, J.B.[Jun-Bao],
Feature space expansion and compression with spatial-spectral
augmentation for hyperspectral image Class-Incremental Learning,
PR(168), 2025, pp. 111830.
Elsevier DOI Code:
WWW Link.
2506
Hyperspectral image classification,
Class incremental learning, Spatial-spectral augmentation, Network compression
BibRef
Zhu, J.[Jitao],
Luo, G.[Guibo],
Duan, B.[Baishan],
Zhu, Y.S.[Yue-Sheng],
Class Incremental Learning With Deep Contrastive Learning and
Attention Distillation,
SPLetters(31), 2024, pp. 1224-1228.
IEEE DOI
2405
Task analysis, Feature extraction, Self-supervised learning,
Data models, Stability criteria, Training, Image classification,
knowledge distillation
BibRef
Song, J.[Jialun],
Chen, J.[Jian],
Du, L.[Lan],
Rebalancing network with knowledge stability for class incremental
learning,
PR(153), 2024, pp. 110506.
Elsevier DOI
2405
Class incremental learning, Catastrophic forgetting,
Class imbalance, Proxy-based metric learning, Knowledge distillation
BibRef
Feng, Z.K.[Zhi-Kun],
Zhou, M.[Mian],
Gao, Z.[Zan],
Stefanidis, A.[Angelos],
Su, J.L.[Jiong-Long],
Dang, K.[Kang],
Li, C.H.[Chuan-Hui],
Adaptive knowledge transfer for class incremental learning,
PRL(183), 2024, pp. 165-171.
Elsevier DOI
2406
Class incremental learning, Knowledge sharing,
Knowledge distillation, Dynamic network
BibRef
Luo, Y.[Yong],
Ge, H.W.[Hong-Wei],
Liu, Y.X.[Yu-Xuan],
Wu, C.G.[Chun-Guo],
Representation Robustness and Feature Expansion for Exemplar-Free
Class-Incremental Learning,
CirSysVideo(34), No. 7, July 2024, pp. 5306-5320.
IEEE DOI
2407
Task analysis, Prototypes, Thermal stability, Feature extraction, Training,
Adaptation models, Data models, Batch normalization, prototype mixing
BibRef
Zhou, Y.H.[Yu-Hang],
Yao, J.C.[Jiang-Chao],
Hong, F.[Feng],
Zhang, Y.[Ya],
Wang, Y.F.[Yan-Feng],
Balanced Destruction-Reconstruction Dynamics for Memory-Replay Class
Incremental Learning,
IP(33), 2024, pp. 4966-4981.
IEEE DOI
2409
Training, Incremental learning, Stability analysis,
Image reconstruction, Benchmark testing, Costs, Thermal stability,
data imbalance
BibRef
Liu, W.Z.[Wen-Zhuo],
Wu, X.J.[Xin-Jian],
Zhu, F.[Fei],
Yu, M.M.[Ming-Ming],
Wang, C.[Chuang],
Liu, C.L.[Cheng-Lin],
Class Incremental Learning with Self-Supervised Pre-Training and
Prototype Learning,
PR(157), 2025, pp. 110943.
Elsevier DOI
2409
Class incremental learning, Catastrophic forgetting,
Prototype learning, Self-supervised learning
BibRef
Guo, H.Y.[Hai-Yang],
Zhu, F.[Fei],
Liu, W.Z.[Wen-Zhuo],
Zhang, X.Y.[Xu-Yao],
Liu, C.L.[Cheng-Lin],
PILORA: Prototype Guided Incremental LORA for Federated
Class-incremental Learning,
ECCV24(LXV: 141-159).
Springer DOI
2412
BibRef
Song, K.[Ke],
Liang, G.Q.[Guo-Qiang],
Chen, Z.J.[Zhao-Jie],
Zhang, Y.N.[Yan-Ning],
Non-Exemplar Class-Incremental Learning by Random Auxiliary Classes
Augmentation and Mixed Features,
CirSysVideo(34), No. 9, September 2024, pp. 7830-7843.
IEEE DOI
2410
Task analysis, Feature extraction, Computational modeling,
Data models, Adaptation models, Data augmentation, noisy prototype
BibRef
Zuo, Y.K.[Yu-Kun],
Yao, H.T.[Han-Tao],
Zhuang, L.S.[Lian-Sheng],
Xu, C.S.[Chang-Sheng],
Hierarchical Augmentation and Distillation for Class Incremental
Audio-Visual Video Recognition,
PAMI(46), No. 11, November 2024, pp. 7348-7362.
IEEE DOI
2410
Data models, Task analysis, Visualization, Feature extraction,
Semantics, Image recognition, Training, Class incremental learning,
hierarchical augmentation and distillation
BibRef
Zhou, D.W.[Da-Wei],
Wang, Q.W.[Qi-Wei],
Qi, Z.H.[Zhi-Hong],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Liu, Z.W.[Zi-Wei],
Class-Incremental Learning: A Survey,
PAMI(46), No. 12, December 2024, pp. 9851-9873.
IEEE DOI
2411
Survey, Incremental Learningx. Task analysis, Training, Surveys, Data models, Birds,
Dogs, Catastrophic forgetting,
lifelong learning
BibRef
Nguyen, L.Q.[Le Quan],
Choi, J.[Jinwoo],
Dang, L.M.[L. Minh],
Moon, H.[Hyeonjoon],
Background debiased class incremental learning for video action
recognition,
IVC(151), 2024, pp. 105295.
Elsevier DOI
2411
Action recognition, Class incremental learning, Debiasing, Temporal shift module
BibRef
Cheng, D.[De],
Zhao, Y.X.[Yu-Xin],
Wang, N.N.[Nan-Nan],
Li, G.Z.[Guo-Zhang],
Zhang, D.W.[Ding-Wen],
Gao, X.B.[Xin-Bo],
Efficient Statistical Sampling Adaptation for Exemplar-Free Class
Incremental Learning,
CirSysVideo(34), No. 11, November 2024, pp. 11451-11463.
IEEE DOI Code:
WWW Link.
2412
Task analysis, Training, Feature extraction,
Prototypes, Memory management, Manifolds, Exemplar-free, adaptation
BibRef
Ni, B.L.[Bo-Lin],
Nie, X.[Xing],
Zhang, C.H.[Cheng-Hao],
Xu, S.X.[Shi-Xiong],
Zhang, X.[Xin],
Meng, G.F.[Gao-Feng],
Xiang, S.M.[Shi-Ming],
MoBoo: Memory-Boosted Vision Transformer for Class-Incremental
Learning,
CirSysVideo(34), No. 11, November 2024, pp. 11169-11183.
IEEE DOI
2412
Task analysis, Transformers, Tensors, Knowledge engineering, Image coding,
Thermal stability, Training, Continual learning, image recognition
BibRef
Liang, G.Q.[Guo-Qiang],
Chen, Z.J.[Zhao-Jie],
Su, S.B.[Shi-Bin],
Zhang, S.Z.[Shi-Zhou],
Zhang, Y.N.[Yan-Ning],
A masking, linkage and guidance framework for online class
incremental learning,
PR(160), 2025, pp. 111185.
Elsevier DOI
2501
Class incremental learning, Logit mask, Feature distillation
BibRef
Wang, Y.[Ye],
Zhao, G.S.[Guo-Shuai],
Qian, X.M.[Xue-Ming],
Improved Continually Evolved Classifiers for Few-Shot
Class-Incremental Learning,
CirSysVideo(34), No. 2, February 2024, pp. 1123-1134.
IEEE DOI
2402
Task analysis, Training, Prototypes, Circuit stability, Power capacitors,
Measurement, Training data, Lifelong learning, cross-attention mechanism
BibRef
Tao, Z.[Zhe],
Yu, L.[Lu],
Yao, H.T.[Han-Tao],
Huang, S.[Shucheng],
Xu, C.S.[Chang-Sheng],
Class Incremental Learning for Light-Weighted Networks,
CirSysVideo(34), No. 12, December 2024, pp. 12210-12220.
IEEE DOI
2501
Task analysis, Neural networks, Knowledge engineering,
Incremental learning, Training, Data models, Streams,
image classification
BibRef
Xian, Y.[Yan],
Yu, H.[Hong],
Li, H.X.[Hua-Xiong],
Wang, G.Y.[Guo-Yin],
Class Incremental Learning via Semantic Information Mapping and
Background Information Calibrating,
CirSysVideo(34), No. 12, December 2024, pp. 13373-13385.
IEEE DOI
2501
Prototypes, Semantics, Feature extraction, Incremental learning,
Task analysis, Training, protein family
BibRef
Zhou, D.W.[Da-Wei],
Cai, Z.W.[Zi-Wen],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Liu, Z.W.[Zi-Wei],
Revisiting Class-Incremental Learning with Pre-Trained Models:
Generalizability and Adaptivity are All You Need,
IJCV(133), No. 3, March 2025, pp. 1012-1032.
Springer DOI
2502
BibRef
Dong, S.L.[Song-Lin],
Gao, X.Y.[Xin-Yuan],
He, Y.H.[Yu-Hang],
Zhou, Z.D.[Zheng-Dong],
Kot, A.C.[Alex C.],
Gong, Y.H.[Yi-Hong],
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar
Class-Incremental Learning,
CirSysVideo(35), No. 4, April 2025, pp. 3146-3159.
IEEE DOI
2504
Prototypes, Feature extraction, Transformers, Absorption,
Computational modeling, Training, Incremental learning, dynamic boundary-aware
BibRef
Qiang, S.Y.[Sun-Yuan],
Yu, X.X.[Xin-Xing],
Liang, Y.Y.[Yan-Yan],
Wan, J.[Jun],
Zhang, D.[Du],
Collaborative Adapter Experts for Class-Incremental Learning,
SPLetters(32), 2025, pp. 1530-1534.
IEEE DOI
2504
Collaboration, Adaptation models, Training, Artificial intelligence,
Prototypes, Indexes, Head, collaborative learning
BibRef
Wang, X.[Xi],
Yang, X.[Xu],
Wei, K.[Kun],
Gu, Y.[Yanan],
Deng, C.[Cheng],
Class Incremental Learning via Contrastive Complementary Augmentation,
IP(34), 2025, pp. 3663-3673.
IEEE DOI
2507
Training, Incremental learning, Contrastive learning, Data models,
Robustness, Feature extraction, Image segmentation,
complementary augmentation
BibRef
Zhu, F.[Fei],
Zhang, X.Y.[Xu-Yao],
Cheng, Z.[Zhen],
Liu, C.L.[Cheng-Lin],
PASS++: A Dual Bias Reduction Framework for Non-Exemplar
Class-Incremental Learning,
PAMI(47), No. 8, August 2025, pp. 7123-7139.
IEEE DOI
2507
Prototypes, Feature extraction, Training data,
Artificial intelligence, Training, Incremental learning,
prototype augmentation
BibRef
Jodelet, Q.[Quentin],
Liu, X.[Xin],
Phua, Y.J.[Yin Jun],
Murata, T.[Tsuyoshi],
Memory augmented using diffusion model for class-incremental learning,
IVC(161), 2025, pp. 105600.
Elsevier DOI
2509
BibRef
Earlier:
Class-Incremental Learning using Diffusion Model for Distillation and
Replay,
VCL23(3417-3425)
IEEE DOI
2401
Continual learning, Class incremental learning,
Image classification, Image generation
BibRef
Zhang, W.T.[Wen-Tao],
Yu, T.[Tong],
Wang, R.X.[Rui-Xuan],
Xie, J.H.[Jian-Hui],
Trucco, E.[Emanuele],
Zheng, W.S.[Wei-Shi],
Yang, X.B.[Xiao-Bo],
Visual Class Incremental Learning With Textual Priors Guidance Based
on an Adapted Vision-Language Model,
MultMed(27), 2025, pp. 5426-5438.
IEEE DOI
2509
Visualization, Adaptation models, Artificial intelligence,
Data models, Training, Biomedical imaging, Semantics, vision-language model
BibRef
Cao, X.S.[Xu-Sheng],
Lu, H.[Haori],
Liu, X.L.[Xia-Lei],
Cheng, M.M.[Ming-Ming],
Class Incremental Learning for Image Classification With
Out-of-Distribution Task Identification,
MultMed(27), 2025, pp. 5507-5520.
IEEE DOI
2509
Incremental learning, Training, Testing, Feature extraction,
Data models, Image classification, Head, Adaptation models,
image-text pretraining
BibRef
Liang, G.Q.[Guo-Qiang],
Su, S.B.[Shi-Bin],
Cheng, D.[De],
Zhang, S.Z.[Shi-Zhou],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Enhancing Feature Learning With Hard Samples in Mutual Learning for
Online Class Incremental Learning,
IP(34), 2025, pp. 6939-6952.
IEEE DOI Code:
WWW Link.
2511
Training, Feature extraction, Data augmentation,
Computational modeling, Memory management, Data mining,
mutual learning
BibRef
Chen, T.[Tieyuan],
Liu, H.[Huabin],
Lim, C.H.[Chern Hong],
See, J.[John],
Gao, X.[Xing],
Hou, J.H.[Jun-Hui],
Lin, W.Y.[Wei-Yao],
CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free
Video Class-Incremental Learning,
CirSysVideo(35), No. 11, November 2025, pp. 11488-11501.
IEEE DOI Code:
WWW Link.
2511
Knowledge engineering, Adaptation models, Training,
Spatiotemporal phenomena, Integrated circuit modeling, causal inference
BibRef
Wang, S.F.[Shao-Fan],
Wang, W.X.[Wei-Xing],
Sun, Y.F.[Yan-Feng],
Wang, Z.Y.[Zhi-Yong],
Wang, B.Y.[Bo-Yue],
Yin, B.C.[Bao-Cai],
Dual-Attention Transformers for Class-Incremental Learning:
A Tale of Two Memories,
MultMed(27), 2025, pp. 8763-8775.
IEEE DOI
2511
Transformers, Long short term memory, Logic gates, Training,
Incremental learning, Hippocampus, Attention mechanisms
BibRef
Hu, Y.J.[Yi-Jie],
Huang, K.[Kaizhu],
Wang, W.[Wei],
Huang, X.W.[Xiao-Wei],
Wang, Q.[Qiufeng],
You look from old classes: Towards accurate few shot
class-incremental learning,
PR(172), 2026, pp. 112352.
Elsevier DOI
2512
Class incremental learning, Few-shot learning,
Few-shot class incremental learning, Catastrophic forgetting, Prototype learning
BibRef
Wang, C.D.[Cheng-Dong],
Ou, Y.J.[Yang-Jun],
Tang, X.F.[Xian-Fang],
Wu, Y.[Yuan],
Shi, W.[Wuxuan],
Liu, X.L.[Xue-Liang],
Yan, R.[Rui],
Class-aware prototype augmentation and decoupled feature distillation
for class-incremental learning,
PR(172), 2026, pp. 112692.
Elsevier DOI Code:
WWW Link.
2601
Class-incremental learning, Prototype learning, Exemplar-free,
Image classification
BibRef
Wang, L.Y.[Le-Yuan],
Xiang, L.[Liuyu],
Wang, Y.L.[Yun-Long],
Wu, H.J.[Hui-Jia],
Yang, H.F.[Hua-Feng],
Liu, J.Q.[Jing-Qian],
He, Z.F.[Zhao-Feng],
Rethinking Class-Incremental Learning From a Dynamic Imbalanced
Learning Perspective,
MultMed(28), 2026, pp. 825-836.
IEEE DOI
2601
Prototypes, Adaptation models, Contrastive learning, Incremental learning,
Representation learning, Memory management, prototype learning
BibRef
He, Z.[Zeyu],
Huang, S.[Shuai],
Lu, Y.[Yuwu],
Zhao, M.[Ming],
MoTiC: momentum tightness and contrast for few-shot class-incremental
learning,
PR(173), 2026, pp. 112753.
Elsevier DOI
2601
Few-shot class incremental learning, Representation learning,
Representation transferability, Momentum network
BibRef
Singh, K.K.[Krishna Kumar],
Bindu, K.H.[K. Hima],
Mitigating bias in Few Shot Class Incremental Learning with Feature
Augmentation and Logits Mix-up,
JVCIR(115), 2026, pp. 104687.
Elsevier DOI
2601
Few shot class incremental learning,
Entropy based logits mix-up, Bias and variance, Missing pass filter
BibRef
Xu, Z.J.[Zheng-Jin],
Li, X.Y.[Xu-Yang],
Chang, X.B.[Xia-Bin],
Zheng, W.S.[Wei-Shi],
Wang, R.X.[Rui-Xuan],
Slowly expanding neural network for class incremental learning,
PR(171), 2026, pp. 112213.
Elsevier DOI
2511
Continual learning, Image classification, Model expansion
BibRef
Xiong, F.Y.[Fang-Ying],
Yuan, Z.Q.[Zhao-Quan],
Wu, X.[Xiao],
Xu, C.S.[Chang-Sheng],
Class-Specific Knowledge-Guided Multimodal Prompt Tuning for Few-Shot
Class-Incremental Learning,
CirSysVideo(36), No. 1, January 2026, pp. 763-776.
IEEE DOI
2602
Power capacitors, Training, Visualization, Adaptation models, Tuning,
Overfitting, Incremental learning, Feature extraction, Accuracy, CLIP
BibRef
Zhou, Y.[Yuan],
Hong, R.C.[Ri-Chang],
Guo, Y.R.[Yan-Rong],
Liu, L.[Lin],
Hao, S.J.[Shi-Jie],
Zhang, H.W.[Han-Wang],
Controllable Relation Disentanglement for Few-Shot Class-Incremental
Learning,
CirSysVideo(36), No. 2, February 2026, pp. 1587-1600.
IEEE DOI
2602
Power capacitors, Training, Visualization, Grippers,
Adaptation models, Semantics, Memory management,
relation disentanglement
BibRef
Yang, Y.B.[Yuan-Bo],
Qu, J.H.[Jia-Hui],
Dong, W.Q.[Wen-Qian],
Huang, L.[Ling],
Li, Y.S.[Yun-Song],
Prototype-Based Meta-Prompt Tuning: Toward Rehearsal-Free Few-Shot
Class-Incremental Learning for Multimodal Remote Sensing Image,
IP(35), 2026, pp. 434-448.
IEEE DOI Code:
WWW Link.
2602
Prototypes, Remote sensing, Adaptation models, Feature extraction,
Data models, Computational modeling, Training, Power capacitors,
meta-learning
BibRef
Fu, Z.H.[Zhi-Han],
Zhang, Z.Q.[Zhi-Qi],
Liao, S.P.[Shi-Peng],
Huang, Z.Y.[Zheng-Yu],
Chen, Z.[Zerun],
Shen, T.Y.[Tian-Yu],
PSR: Proactive soft-orthogonal regulation for long-tailed
class-incremental learning,
PR(176), 2026, pp. 113207.
Elsevier DOI
2603
Long-tailed class-incremental learning,
Tail-class representation, Feature space regulation
BibRef
Wei, Y.Z.[Yun-Ze],
Liu, Y.H.[Yu-Han],
Niu, B.[Ben],
Xiang, X.[Xiantai],
Lin, J.[Jingdun],
Hu, Y.X.[Yu-Xin],
Wu, Y.R.[Yi-Rong],
A Memory-Efficient Class-Incremental Learning Framework for Remote
Sensing Scene Classification via Feature Replay,
RS(18), No. 6, 2026, pp. 896.
DOI Link
2603
BibRef
Gao, Z.J.[Zi-Jian],
Xu, K.[Kele],
Zhang, X.X.[Xing-Xing],
Zhuang, H.P.[Hui-Ping],
Wan, T.J.[Tian-Jiao],
Ding, B.[Bo],
Mao, X.J.[Xin-Jun],
Wang, H.[Huaimin],
Rethinking Obscured Sub-Optimality in Analytic Learning for
Exemplar-Free Class-Incremental Learning,
CirSysVideo(36), No. 4, April 2026, pp. 4287-4301.
IEEE DOI
2604
Feature extraction, Prototypes, Training, Thermal stability,
Circuit stability, Optimization, Overfitting, Adaptation models,
analytic learning
BibRef
Chen, B.Z.[Bing-Zhi],
Chen, Z.M.[Zhi-Ming],
Cai, S.[Sudong],
Fang, X.Z.[Xiao-Zhao],
Bennamoun, M.[Mohammed],
Xie, S.L.[Sheng-Li],
Toward Bidirectional Adaptability for Few-Shot Class-Incremental
Learning With Forward-Backward Knowledge Transfer,
MultMed(28), 2026, pp. 2337-2351.
IEEE DOI
2604
Power capacitors, Adaptation models, Knowledge transfer,
Incremental learning, Artificial intelligence, Training, Semantics,
knowledge transfer
BibRef
Zhang, H.[Huan],
Lyu, F.[Fan],
Fan, S.H.[Sheng-Hua],
Zheng, Y.J.[Yu-Jin],
Wang, D.W.[Ding-Wen],
Constructing Enhanced Mutual Information for Online Class-Incremental
Learning,
MultMed(28), 2026, pp. 2954-2969.
IEEE DOI
2604
Mutual information, Electromagnetic interference, Training,
Knowledge engineering, Data models, Prototypes,
mutual information and catastrophic forgetting
BibRef
Huang, S.[Shuai],
Lin, X.[Xuhan],
Lu, Y.[Yuwu],
CASP: Few-shot class-incremental learning with CLS token attention
steering prompts,
PR(177), 2026, pp. 113306.
Elsevier DOI
2605
Few-shot class incremental learning, Catastrophic forgetting,
Prompt tuning, Pretrained knowledge transfer, Feature generalization
BibRef
Su, S.B.[Shi-Bin],
Liang, G.Q.[Guo-Qiang],
Cheng, D.[De],
Zhang, S.Z.[Shi-Zhou],
Ran, L.Y.[Ling-Yan],
Multi-Level Collaborative Distillation Meets Global Workspace Model:
A Unified Framework for OCIL,
IP(35), 2026, pp. 5583-5596.
IEEE DOI Code:
WWW Link.
2606
Modeling, Memory, Erbium, FAA, Learning (artificial intelligence),
Training, Broadcasting, Online class incremental learning,
plasticity and stability balance
BibRef
Du, K.[Kaile],
Xie, J.Z.[Jun-Zhou],
Lyu, F.[Fan],
Zhou, Y.F.[Yi-Fan],
Ye, Z.[Zihan],
Li, W.[Wei],
Li, Y.Y.[Yu-Yang],
Liu, G.C.[Guang-Can],
Negative-weighted knowledge distillation regularized graph
convolutional network for multi-label class-incremental learning,
PR(179), 2026, pp. 113657.
Elsevier DOI
2606
MLCIL, ALA, NKD, GCN
BibRef
Zou, Y.[Yi],
Qu, J.H.[Jia-Hui],
Dong, W.Q.[Wen-Qian],
Li, Y.S.[Yun-Song],
BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for
Class-Incremental Learning on Multisource Remote Sensing Images,
IP(35), 2026, pp. 7076-7089.
IEEE DOI Code:
WWW Link.
2607
Modeling, Learning (artificial intelligence), Training,
Incremental learning, Tuning, Computers, Remote sensing, Multi-model,
prompt learning
BibRef
Zheng, B.[Bowen],
Shen, Z.J.[Zi-Jun],
Zhou, D.W.[Da-Wei],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Bi-Compatible Task-Agnostic Feature Augmentation for Expansion-Based
Class-Incremental Learning,
IJCV(134), No. 8, August 2026, pp. 366.
Springer DOI Code:
WWW Link.
2608
BibRef
Earlier: A1, A3, A4, A5, Only:
Task-Agnostic Guided Feature Expansion for Class-Incremental Learning,
CVPR25(10099-10109)
IEEE DOI Code:
WWW Link.
2508
Training, Learning systems, Adaptation models, Visualization, Codes,
Aggregates, Computational modeling, Memory management,
class-incremental learning
BibRef
Jiang, K.[Kai],
Lin, Z.[Zisong],
Zhang, H.Y.[Hong-Yuan],
Bai, X.[Xueru],
Li, X.[Xuelong],
Miles: Metric Learning With Expandable Subspace for Pre-Trained
Model-Based Class-Incremental Learning,
IP(35), 2026, pp. 8291-8305.
IEEE DOI
2608
Modeling, Training, Learning (artificial intelligence),
Distance measurement, Prototypes, Accuracy, Incremental learning,
expandable subspace
BibRef
Xu, F.K.[Fan-Kang],
Jin, L.[Lu],
Sun, Y.P.[Yan-Peng],
Li, Z.C.[Ze-Chao],
Preserving Fairness in Knowledge Transfer for Exemplar-Free
Class-Incremental Learning via Semantic Propagation,
IP(35), 2026, pp. 9198-9211.
IEEE DOI
2609
Modeling, Labeling, Knowledge transfer, Propagation, Semantics,
Incremental learning, Training, Accuracy, Prototypes,
label enhancement
BibRef
Wang, S.[Shaofan],
Guo, P.L.[Peng-Li],
Wang, W.X.[Wei-Xing],
Sun, Y.F.[Yan-Feng],
Yin, B.C.[Bao-Cai],
Debiased Hypernetworks Are Generative Class-Incremental Learners,
MultMed(28), 2026, pp. 7401-7413.
IEEE DOI
2609
Adaptation models, Training, Transformers, Incremental learning,
Stability analysis, Knowledge engineering,
vision transformer
BibRef
Karantaidis, G.[George],
Pantsios, A.[Athanasios],
Kompatsiaris, I.[Ioannis],
Papadopoulos, S.[Symeon],
Few-Shot Class-Incremental Learning for Efficient SAR Automatic
Target Recognition,
ICIP25(1534-1539)
IEEE DOI
2601
Filters, Target recognition, Discrete Fourier transforms,
Computer architecture, Benchmark testing, Feature extraction, MSTAR
BibRef
Kim, S.[Seongsu],
Yun, S.[Sangwoo],
Kin, I.[Illhwan],
Lee, D.[Dongheon],
Paik, J.[Joonki],
Session Class Prototype Incremental Learning (SCPIL): Mitigating
Catastrophic Forgetting with Distance-Based Prototype Learning,
ICIP25(929-934)
IEEE DOI
2601
Metalearning, Solid modeling, Incremental learning,
Transfer learning, Prototypes, Interference, Solids,
session prototypes
BibRef
Khazaei, E.[Ensieh],
Hatzinakos, D.[Dimitrios],
Forget Less, Learn More: Contrastive-Based Federated Class
Incremental Learning with a Low-Dimensional Projection Layer,
FedVision25(1782-1791)
IEEE DOI Code:
WWW Link.
2512
Training, Data privacy, Accuracy, Incremental learning,
Federated learning, Contrastive learning, Feature extraction,
Stability plasticity
BibRef
Ling, S.[Shimou],
Gan, S.K.[Sheng-Kai],
Wang, C.[Caoxin],
Pan, L.[Lili],
Li, H.L.[Hong-Liang],
Enhancing Few-Shot Class-Incremental Learning via Frozen Feature
Augmentation,
Anti-UAV25(6612-6620)
IEEE DOI Code:
WWW Link.
2512
Representation learning, Incremental learning, Codes,
Stability analysis, Power capacitors, Overfitting
BibRef
Tian, Z.Y.[Zhen-Ya],
Xiao, J.[Jun],
Liu, L.P.[Lu-Peng],
Jiang, H.Y.[Hai-Yong],
Activating Sparse Part Concepts for 3D Class Incremental Learning,
CVPR25(30343-30353)
IEEE DOI Code:
WWW Link.
2508
Training, Adaptation models, Solid modeling, Incremental learning,
Shape, Computational modeling, Benchmark testing, Transformers
BibRef
He, J.P.[Jiang-Peng],
Duan, Z.H.[Zhi-Hao],
Zhu, F.Q.[Feng-Qing],
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free
Class-Incremental Learning,
CVPR25(30534-30544)
IEEE DOI
2508
Training, Adaptation models, Systematics,
Computational modeling, Redundancy, Interference,
class-incremental learning
BibRef
Li, J.S.[Jia-Shuo],
Wang, S.K.[Shao-Kun],
Qian, B.[Bo],
He, Y.H.[Yu-Hang],
Wei, X.[Xing],
Wang, Q.[Qiang],
Gong, Y.H.[Yi-Hong],
Dynamic Integration of Task-Specific Adapters for Class Incremental
Learning,
CVPR25(30545-50555)
IEEE DOI
2508
Hands, Adaptation models, Visualization, Privacy, Accuracy,
Incremental learning, Computational modeling, Benchmark testing, optimization
BibRef
Zhang, Y.F.[Yi-Fei],
Zhu, H.[Hao],
Tan, A.Z.[Alysa Ziying],
Yu, D.[Dianzhi],
Huang, L.T.[Long-Tao],
Yu, H.[Han],
pFedMxF: Personalized Federated Class-incremental Learning with
Mixture of Frequency Aggregation,
CVPR25(30640-30650)
IEEE DOI
2508
Costs, Incremental learning, Federated learning,
Frequency-domain analysis, Collaboration, Interference,
Pattern recognition
BibRef
Gao, Z.J.[Zi-Jian],
Jia, W.W.[Wang-Wang],
Zhang, X.X.[Xing-Xing],
Zhou, D.[Dulan],
Xu, K.[Kele],
Dawei, F.[Feng],
Dou, Y.[Yong],
Mao, X.J.[Xin-Jun],
Wang, H.[Huaimin],
Knowledge Memorization and Rumination for Pre-trained Model-based
Class-Incremental Learning,
CVPR25(20523-20533)
IEEE DOI
2508
Interpolation, Adaptation models, Incremental learning,
Feature extraction, Stability analysis, Intelligent systems,
pre-trained models.
BibRef
Hwang, S.H.[Seong-Hyeon],
Kim, M.[Minsu],
Whang, S.E.[Steven Euijong],
T-CIL: Temperature Scaling using Adversarial Perturbation for
Calibration in Class-Incremental Learning,
CVPR25(15339-15348)
IEEE DOI
2508
Training, Accuracy, Perturbation methods, Calibration, Optimization
BibRef
Chen, H.T.[Hui-Tong],
Wang, Y.[Yu],
Fan, Y.[Yan],
Jiang, G.S.[Guo-Song],
Hu, Q.H.[Qing-Hua],
Reducing Class-wise Confusion for Incremental Learning with
Disentangled Manifolds,
CVPR25(10121-10130)
IEEE DOI Code:
WWW Link.
2508
Manifolds, Incremental learning, Codes, Prototypes,
Stability analysis, class incremental learning, continual learning
BibRef
Zou, X.H.[Xiao-Han],
Ma, W.C.[Wen-Chao],
Zhao, S.[Shu],
Learning Conditional Space-Time Prompt Distributions for Video
Class-Incremental Learning,
CVPR25(4862-4873)
IEEE DOI Code:
WWW Link.
2508
Analytical models, Codes, Computational modeling,
Benchmark testing, Transformers, Diffusion models, Boosting, Videos
BibRef
Fukuda, T.[Takuma],
Kera, H.[Hiroshi],
Kawamoto, K.[Kazuhiko],
Adapter Merging with Centroid Prototype Mapping for Scalable
Class-Incremental Learning,
CVPR25(4884-4893)
IEEE DOI Code:
WWW Link.
2508
Accuracy, Codes, Scalability, Merging, Prototypes, Benchmark testing
BibRef
Lai, G.[Guannan],
Li, Y.J.[Yu-Jie],
Wang, X.K.[Xiang-Kun],
Zhang, J.[Junbo],
Li, T.R.[Tian-Rui],
Yang, X.[Xin],
Order-Robust Class Incremental Learning: Graph-Driven Dynamic
Similarity Grouping,
CVPR25(4894-4904)
IEEE DOI Code:
WWW Link.
2508
Sensitivity, Incremental learning, Codes, Heuristic algorithms,
Computational modeling, Buildings, Robustness,
continual learning
BibRef
Yang, Y.[Yi],
Zhong, L.[Lei],
Zhuang, H.P.[Hui-Ping],
ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental
Learning,
WACV25(3396-3405)
IEEE DOI Code:
WWW Link.
2505
Point cloud compression, Representation learning,
Solid modeling, Incremental learning,
3D Multi-modal Learning
BibRef
Kalla, J.[Jayateja],
Kumar, R.[Rohit],
Biswas, S.[Soma],
TACLE: Task and Class-Aware Exemplar-Free Semi-Supervised Class
Incremental Learning,
WACV25(6944-6954)
IEEE DOI Code:
WWW Link.
2505
Adaptation models, Incremental learning, Codes, Benchmark testing,
Data models, Standards, class incremental learning,
task-adaptive threshold
BibRef
Feillet, E.[Eva],
Popescu, A.[Adrian],
Hudelot, C.[Céline],
A Reality Check on Pre-training for Exemplar-free Class-Incremental
Learning,
WACV25(7625-7636)
IEEE DOI
2505
Training, Visualization, Accuracy, Feature extraction,
Data augmentation, Transformers, Data models,
class-incremental learning
BibRef
Meng, Z.C.[Zi-Chong],
Zhang, J.[Jie],
Yang, C.D.[Chang-Di],
Zhan, Z.[Zheng],
Zhao, P.[Pu],
Wang, Y.Z.[Yan-Zhi],
Diffclass: Diffusion-based Class Incremental Learning,
ECCV24(LXXXVII: 142-159).
Springer DOI
2412
BibRef
Park, M.Y.[Min-Yeong],
Lee, J.H.[Jae-Ho],
Park, G.M.[Gyeong-Moon],
Versatile Incremental Learning: Towards Class and Domain-agnostic
Incremental Learning,
ECCV24(XXXI: 271-288).
Springer DOI
2412
BibRef
Du, K.[Kaile],
Zhou, Y.F.[Yi-Fan],
Lyu, F.[Fan],
Li, Y.Y.[Yu-Yang],
Lu, C.[Chen],
Liu, G.C.[Guang-Can],
Confidence Self-calibration for Multi-label Class-incremental Learning,
ECCV24(XXXI: 234-252).
Springer DOI
2412
BibRef
Cheng, H.[Hao],
Yang, S.Y.[Si-Yuan],
Wang, C.[Chong],
Zhou, J.T.Y.[Joey Tian-Yi],
Kot, A.C.[Alex C.],
Wen, B.H.[Bi-Han],
STSP: Spatial-temporal Subspace Projection for Video Class-incremental
Learning,
ECCV24(XXVIII: 374-391).
Springer DOI
2412
BibRef
Han, J.[Jisu],
Na, J.[Jaemin],
Hwang, W.J.[Won-Jun],
SRIL: Selective Regularization for Class-incremental Learning,
ACCV24(VIII: 351-367).
Springer DOI
2412
BibRef
Yoo, M.K.[Min Kyoon],
Park, Y.R.[Yu Rang],
Federated Class Incremental Learning:
A Pseudo Feature Based Approach Without Exemplars,
ACCV24(VII: 354-365).
Springer DOI
2412
BibRef
Kurpukdee, N.[Nattapong],
Bors, A.G.[Adrian G.],
Temporal Transformer Encoder for Video Class Incremental Learning,
ICIP24(1295-1301)
IEEE DOI
2411
Training, Incremental learning, Databases, Memory management,
Mixture models, Streaming media, Transformers,
video class incremental learning
BibRef
Zhou, D.W.[Da-Wei],
Cai, Z.W.[Zi-Wen],
Ye, H.J.[Han-Jia],
Zhang, L.J.[Li-Jun],
Zhan, D.C.[De-Chuan],
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental
Learning,
CVPR25(20547-20557)
IEEE DOI Code:
WWW Link.
2508
Learning systems, Adaptation models, Solid modeling, Merging, Ducts,
Semantics, Solids, Vectors, Electronics packaging,
catastrophic forgetting
BibRef
Zhou, D.W.[Da-Wei],
Sun, H.L.[Hai-Long],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Expandable Subspace Ensemble for Pre-Trained Model-Based
Class-Incremental Learning,
CVPR24(23554-23564)
IEEE DOI Code:
WWW Link.
2410
Learning systems, Adaptation models, Incremental learning,
Computational modeling, Semantics, Decision making,
Pre-Trained Model
BibRef
Wen, H.T.[Hai-Tao],
Pan, L.[Lili],
Dai, Y.[Yu],
Qiu, H.Q.[He-Qian],
Wang, L.X.[Lan-Xiao],
Wu, Q.B.[Qing-Bo],
Li, H.L.[Hong-Liang],
Class Incremental Learning with Multi-Teacher Distillation,
CVPR24(28443-28452)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Incremental learning, Codes,
Perturbation methods, Memory management, Teleportation,
Multi-Teacher Distillation
BibRef
He, Y.H.[Yu-Hang],
Chen, Y.J.[Ying-Jie],
Jin, Y.H.[Yu-Han],
Dong, S.L.[Song-Lin],
Wei, X.[Xing],
Gong, Y.H.[Yi-Hong],
DYSON: Dynamic Feature Space Self-Organization for Online Task-Free
Class Incremental Learning,
CVPR24(23741-23751)
IEEE DOI Code:
WWW Link.
2410
Geometry, Upper bound, Incremental learning, Heuristic algorithms,
Source coding, Prototypes, Feature extraction, continual learning,
feature space organization
BibRef
Liu, X.L.[Xia-Lei],
Zhai, J.T.[Jiang-Tian],
Bagdanov, A.D.[Andrew D.],
Li, K.[Ke],
Cheng, M.M.[Ming-Ming],
Task-Adaptive Saliency Guidance for Exemplar-Free Class Incremental
Learning,
CVPR24(23954-23963)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Data privacy, Incremental learning, Codes, Noise,
Performance gain
BibRef
Luo, Y.T.[Yu-Tian],
Zhao, S.Q.[Shi-Qi],
Wu, H.R.[Hao-Ran],
Lu, Z.W.[Zhi-Wu],
Dual-Enhanced Coreset Selection with Class-Wise Collaboration for
Online Blurry Class Incremental Learning,
CVPR24(23995-24004)
IEEE DOI
2410
Incremental learning, Adaptive systems, Navigation, Collaboration,
Benchmark testing, Boosting, continual learning, online learning,
class incremental learning
BibRef
Qiu, Z.[Zihuan],
Xu, Y.[Yi],
Meng, F.M.[Fan-Man],
Li, H.L.[Hong-Liang],
Xu, L.F.[Lin-Feng],
Wu, Q.B.[Qing-Bo],
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental
Learning,
CVPR24(24025-24035)
IEEE DOI
2410
Training, Incremental learning, Accuracy, Image recognition,
Semantics, Prototypes, Class Incremental Learning, Continual Learning
BibRef
He, J.P.[Jiang-Peng],
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning,
CVPR24(16668-16677)
IEEE DOI
2410
Degradation, Protocols, Computational modeling, Training data,
Data models, Robustness, continual learniing, long-tailed learning,
imbalanced gradient
BibRef
Gurbuz, M.B.[Mustafa Burak],
Moorman, J.M.[Jean Michael],
Dovrolis, C.[Constantine],
NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class
Incremental Learning,
CVPR24(23659-23669)
IEEE DOI Code:
WWW Link.
2410
Incremental learning, Neurons,
Artificial neural networks, Encoding, Replay-free
BibRef
Li, Q.W.[Qi-Wei],
Peng, Y.X.[Yu-Xin],
Zhou, J.H.[Jia-Huan],
FCS: Feature Calibration and Separation for Non-Exemplar Class
Incremental Learning,
CVPR24(28495-28504)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Incremental learning, Prototypes,
Transfer functions, Contrastive learning, Distortion
BibRef
Kim, J.[Junsu],
Ku, Y.H.[Yun-Hoe],
Kim, J.[Jihyeon],
Cha, J.[Junuk],
Baek, S.[Seungryul],
VLM-PL: Advanced Pseudo Labeling approach for Class Incremental
Object Detection via Vision-Language Model,
CLVision24(4170-4181)
IEEE DOI
2410
Training, Incremental learning, Computational modeling,
Object detection, Detectors, Class Incremental Object Detection,
Pseudo Labeling
BibRef
Cao, X.S.[Xu-Sheng],
Lu, H.R.[Hao-Ri],
Huang, L.[Linlan],
Liu, X.L.[Xia-Lei],
Cheng, M.M.[Ming-Ming],
Generative Multi-modal Models are Good Class-Incremental Learners,
CVPR24(28706-28717)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Head, Codes,Accuracy
BibRef
Gao, X.[Xin],
Yang, X.[Xin],
Yu, H.[Hao],
Kang, Y.[Yan],
Li, T.R.[Tian-Rui],
FedProK: Trustworthy Federated Class-Incremental Learning via
Prototypical Feature Knowledge Transfer,
FedVision24(4205-4214)
IEEE DOI
2410
Privacy, Data privacy, Federated learning, Prototypes,
Benchmark testing, Federated Learning, Continual Learning
BibRef
Korycki, L.[Lukasz],
Krawczyk, B.[Bartosz],
Class-Incremental Mixture of Gaussians for Deep Continual Learning,
CLVision24(4097-4106)
IEEE DOI
2410
Mixture models, Feature extraction, Data models, continual learning,
deep learning
BibRef
Goswami, D.[Dipam],
Twardowski, B.[Bartlomiej],
van de Weijer, J.[Joost],
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental
Learning with Pre-trained Vision Transformers,
CLVision24(4075-4084)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Training data, Benchmark testing, Feature extraction,
Transformers, Data models, Continual Learning,
Few-Shot Class-Incremental Learning
BibRef
Kim, J.[Junsu],
Cho, H.[Hoseong],
Kim, J.[Jihyeon],
Tiruneh, Y.Y.[Yihalem Yimolal],
Baek, S.[Seungryul],
SDDGR: Stable Diffusion-Based Deep Generative Replay for Class
Incremental Object Detection,
CVPR24(28772-28781)
IEEE DOI
2410
Text to image, Object detection, Regulation,
Computational efficiency, Complexity theory, Continual learning,
deep generative
BibRef
Liu, Y.Y.[Yao-Yao],
Li, Y.Y.[Ying-Ying],
Schiele, B.[Bernt],
Sun, Q.[Qianru],
Wakening Past Concepts without Past Data:
Class-Incremental Learning from Online Placebos,
WACV24(2215-2224)
IEEE DOI
2404
Training, Learning systems, Adaptation models, Costs,
Markov decision processes, Memory management, Streaming media,
Image recognition and understanding
BibRef
Li, S.[Shiyao],
Ning, X.F.[Xue-Fei],
Zhang, S.H.[Shang-Hang],
Guo, L.[Lidong],
Zhao, T.C.[Tian-Chen],
Yang, H.Z.[Hua-Zhong],
Wang, Y.[Yu],
TCP: Triplet Contrastive-relationship Preserving for
Class-Incremental Learning,
WACV24(2020-2029)
IEEE DOI
2404
Self-supervised learning, Artificial neural networks, Algorithms,
Machine learning architectures, formulations, and algorithms
BibRef
Shi, W.[Wuxuan],
Ye, M.[Mang],
Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation
for Non-Exemplar Class-Incremental Learning,
ICCV23(1772-1781)
IEEE DOI
2401
BibRef
Pei, Y.X.[Yi-Xuan],
Qing, Z.W.[Zhi-Wu],
Zhang, S.W.[Shi-Wei],
Wang, X.[Xiang],
Zhang, Y.Y.[Ying-Ya],
Zhao, D.L.[De-Li],
Qian, X.M.[Xue-Ming],
Space-time Prompting for Video Class-incremental Learning,
ICCV23(11898-11908)
IEEE DOI
2401
BibRef
Dong, J.H.[Jia-Hua],
Liang, W.Q.[Wen-Qi],
Cong, Y.[Yang],
Sun, G.[Gan],
Heterogeneous Forgetting Compensation for Class-Incremental Learning,
ICCV23(11708-11717)
IEEE DOI Code:
WWW Link.
2401
BibRef
Moon, J.Y.[Jun-Yeong],
Park, K.H.[Keon-Hee],
Kim, J.U.[Jung Uk],
Park, G.M.[Gyeong-Moon],
Online Class Incremental Learning on Stochastic Blurry Task Boundary
via Mask and Visual Prompt Tuning,
ICCV23(11697-11707)
IEEE DOI Code:
WWW Link.
2401
BibRef
Panos, A.[Aristeidis],
Kobe, Y.[Yuriko],
Reino, D.O.[Daniel Olmeda],
Aljundi, R.[Rahaf],
Turner, R.E.[Richard E.],
First Session Adaptation: A Strong Replay-Free Baseline for
Class-Incremental Learning,
ICCV23(18774-18784)
IEEE DOI
2401
BibRef
Chen, X.W.[Xiu-Wei],
Chang, X.B.[Xia-Bin],
Dynamic Residual Classifier for Class Incremental Learning,
ICCV23(18697-18706)
IEEE DOI
2401
BibRef
Rymarczyk, D.[Dawid],
van de Weijer, J.[Joost],
Zielinski, B.[Bartosz],
Twardowski, B.[Bartlomiej],
ICICLE: Interpretable Class Incremental Continual Learning,
ICCV23(1887-1898)
IEEE DOI
2401
BibRef
Pian, W.G.[Wei-Guo],
Mo, S.T.[Shen-Tong],
Guo, Y.H.[Yun-Hui],
Tian, Y.P.[Ya-Peng],
Audio-Visual Class-Incremental Learning,
ICCV23(7765-7777)
IEEE DOI Code:
WWW Link.
2401
BibRef
Mo, S.T.[Shen-Tong],
Pian, W.G.[Wei-Guo],
Tian, Y.P.[Ya-Peng],
Class-Incremental Grouping Network for Continual Audio-Visual
Learning,
ICCV23(7754-7764)
IEEE DOI Code:
WWW Link.
2401
BibRef
Camuffo, E.[Elena],
Milani, S.[Simone],
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental
and Coarse-to-Fine strategies on Sparse Data,
CLVision23(2447-2456)
IEEE DOI
2309
BibRef
Lin, H.[Huiwei],
Zhang, B.Q.[Bao-Quan],
Feng, S.S.[Shan-Shan],
Li, X.[Xutao],
Ye, Y.M.[Yun-Ming],
PCR: Proxy-Based Contrastive Replay for Online Class-Incremental
Continual Learning,
CVPR23(24246-24255)
IEEE DOI
2309
BibRef
Kanagarajah, S.[Sathursan],
Ambegoda, T.[Thanuja],
Rodrigo, R.[Ranga],
SATHUR: Self Augmenting Task Hallucinal Unified Representation for
Generalized Class Incremental Learning,
VCL23(3465-3472)
IEEE DOI
2401
BibRef
Xiang, J.L.[Jin-Lin],
Shlizerman, E.[Eli],
TKIL: Tangent Kernel Optimization for Class Balanced Incremental
Learning,
VCL23(3521-3531)
IEEE DOI
2401
BibRef
Xu, J.[Jiawen],
Grohnfeldt, C.[Claas],
Kao, O.[Odej],
OpenIncrement: A Unified Framework for Open Set Recognition and Deep
Class-Incremental Learning,
VCL23(3295-3303)
IEEE DOI
2401
BibRef
Zhao, Y.L.[Yun-Long],
Deng, X.H.[Xiao-Heng],
Pei, X.J.[Xin-Jun],
Chen, X.C.[Xue-Chen],
Li, D.[Deng],
Parallel Gradient Blend for Class Incremental Learning,
ICIP23(1220-1224)
IEEE DOI
2312
BibRef
Song, X.[Xiang],
Shu, K.[Kuang],
Dong, S.L.[Song-Lin],
Cheng, J.[Jie],
Wei, X.[Xing],
Gong, Y.H.[Yi-Hong],
Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental
Learning,
WACV24(2378-2387)
IEEE DOI
2404
Adaptation models, Decoding, Algorithms,
Machine learning architectures, formulations, and algorithms,
Image recognition and understanding
BibRef
Dong, S.L.[Song-Lin],
Luo, H.Y.[Hao-Yu],
He, Y.H.[Yu-Hang],
Wei, X.[Xing],
Cheng, J.[Jie],
Gong, Y.H.[Yi-Hong],
Knowledge Restore and Transfer for Multi-Label Class-Incremental
Learning,
ICCV23(18665-18674)
IEEE DOI Code:
WWW Link.
2401
BibRef
Gao, X.Y.[Xin-Yuan],
He, Y.H.[Yu-Hang],
Dong, S.L.[Song-Lin],
Cheng, J.[Jie],
Wei, X.[Xing],
Gong, Y.H.[Yi-Hong],
DKT: Diverse Knowledge Transfer Transformer for Class Incremental
Learning,
CVPR23(24236-24245)
IEEE DOI
2309
BibRef
Cai, T.H.[Teng-Hao],
Zhang, Z.Z.[Zhi-Zhong],
Tan, X.[Xin],
Qu, Y.Y.[Yan-Yun],
Jiang, G.[Guannan],
Wang, C.J.[Cheng-Jie],
Xie, Y.[Yuan],
Multi-Centroid Task Descriptor for Dynamic Class Incremental
Inference,
CVPR23(7298-7307)
IEEE DOI
2309
BibRef
Hu, Z.Y.[Zhi-Yuan],
Li, Y.S.[Yun-Sheng],
Lyu, J.C.[Jian-Cheng],
Gao, D.[Dashan],
Vasconcelos, N.M.[Nuno M.],
Dense Network Expansion for Class Incremental Learning,
CVPR23(11858-11867)
IEEE DOI
2309
BibRef
Cha, S.M.[Sung-Min],
Cho, S.J.[Sung-Jun],
Hwang, D.[Dasol],
Hong, S.[Sunwon],
Lee, M.[Moontae],
Moon, T.[Taesup],
Rebalancing Batch Normalization for Exemplar-Based Class-Incremental
Learning,
CVPR23(20127-20136)
IEEE DOI
2309
BibRef
Kim, D.[Dongwan],
Han, B.H.[Bo-Hyung],
On the Stability-Plasticity Dilemma of Class-Incremental Learning,
CVPR23(20196-20204)
IEEE DOI
2309
BibRef
Petit, G.[Grégoire],
Soumm, M.[Michael],
Feillet, E.[Eva],
Popescu, A.[Adrian],
Delezoide, B.[Bertrand],
Picard, D.[David],
Hudelot, C.[Céline],
An Analysis of Initial Training Strategies for Exemplar-Free
Class-Incremental Learning,
WACV24(1826-1836)
IEEE DOI
2404
Training, Statistical analysis, Transfer learning, Data models,
Stability analysis, Classification algorithms, Algorithms,
Embedded sensing / real-time techniques
BibRef
Petit, G.[Grégoire],
Popescu, A.[Adrian],
Schindler, H.[Hugo],
Picard, D.[David],
Delezoide, B.[Bertrand],
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental
Learning,
WACV23(3900-3909)
IEEE DOI
2302
Performance evaluation, Location awareness, Codes, Filtering,
Feature extraction, Generators, Stability analysis,
Vision + language and/or other modalities
BibRef
Feillet, E.[Eva],
Petit, G.[Grégoire],
Popescu, A.[Adrian],
Reyboz, M.[Marina],
Hudelot, C.[Céline],
AdvisIL - A Class-Incremental Learning Advisor,
WACV23(2399-2408)
IEEE DOI
2302
Learning systems, Adaptation models, Codes, Memory management,
Training data, Algorithms: Machine learning architectures, visual reasoning)
BibRef
Li, Y.[Yinan],
Luo, R.H.[Rong-Hua],
Huang, Z.M.[Zhen-Ming],
Class Incremental Learning based on Local Structure Constraints in
Feature Space,
ICPR22(2056-2062)
IEEE DOI
2212
Deep learning, Learning systems, Degradation, Training data,
Stability analysis, Robustness, Classification algorithms
BibRef
Ashok, A.[Arjun],
Joseph, K.J.,
Balasubramanian, V.N.[Vineeth N.],
Class-Incremental Learning with Cross-Space Clustering and Controlled
Transfer,
ECCV22(XXVII:105-122).
Springer DOI
2211
BibRef
Wang, F.Y.[Fu-Yun],
Zhou, D.W.[Da-Wei],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
FOSTER: Feature Boosting and Compression for Class-Incremental Learning,
ECCV22(XXV:398-414).
Springer DOI
2211
BibRef
Gao, Q.[Qiankun],
Zhao, C.[Chen],
Ghanem, B.[Bernard],
Zhang, J.[Jian],
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class
Incremental Learning,
ECCV22(XXIII:423-439).
Springer DOI
2211
BibRef
Liu, X.L.[Xia-Lei],
Hu, Y.S.[Yu-Song],
Cao, X.S.[Xu-Sheng],
Bagdanov, A.D.[Andrew D.],
Li, K.[Ke],
Cheng, M.M.[Ming-Ming],
Long-Tailed Class Incremental Learning,
ECCV22(XXXIII:495-512).
Springer DOI
2211
BibRef
Gu, Y.[Yanan],
Yang, X.[Xu],
Wei, K.[Kun],
Deng, C.[Cheng],
Not Just Selection, but Exploration: Online Class-Incremental
Continual Learning via Dual View Consistency,
CVPR22(7432-7441)
IEEE DOI
2210
Training, Representation learning, Semantics, Neural networks,
Benchmark testing, Streaming media, Recognition: detection,
Representation learning
BibRef
Bhunia, A.K.[Ayan Kumar],
Gajjala, V.R.[Viswanatha Reddy],
Koley, S.[Subhadeep],
Kundu, R.[Rohit],
Sain, A.[Aneeshan],
Xiang, T.[Tao],
Song, Y.Z.[Yi-Zhe],
Doodle It Yourself: Class Incremental Learning by Drawing a Few
Sketches,
CVPR22(2283-2292)
IEEE DOI
2210
Knowledge engineering, Ethics, Visualization,
Technological innovation, Privacy, Data privacy, Message passing,
Vision applications and systems
BibRef
Zhu, K.[Kai],
Zheng, K.C.[Ke-Cheng],
Feng, R.L.[Rui-Li],
Zhao, D.L.[De-Li],
Cao, Y.[Yang],
Zha, Z.J.[Zheng-Jun],
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental
Learning,
ICCV23(19147-19156)
IEEE DOI
2401
BibRef
Zhu, K.[Kai],
Zhai, W.[Wei],
Cao, Y.[Yang],
Luo, J.B.[Jie-Bo],
Zha, Z.J.[Zheng-Jun],
Self-Sustaining Representation Expansion for Non-Exemplar
Class-Incremental Learning,
CVPR22(9286-9295)
IEEE DOI
2210
Fuses, Prototypes, Benchmark testing,
Task analysis, Optimization,
Representation learning
BibRef
Shi, Y.J.[Yu-Jun],
Zhou, K.Q.[Kuang-Qi],
Liang, J.[Jian],
Jiang, Z.H.[Zi-Hang],
Feng, J.S.[Jia-Shi],
Torr, P.H.S.[Philip H.S.],
Bai, S.[Song],
Tan, V.Y.F.[Vincent Y.F.],
Mimicking the Oracle: An Initial Phase Decorrelation Approach for
Class Incremental Learning,
CVPR22(16701-16710)
IEEE DOI
2210
Training, Representation learning, Codes, Computational modeling,
Benchmark testing, Representation learning,
retrieval
BibRef
Smith, J.[James],
Hsu, Y.C.[Yen-Chang],
Balloch, J.[Jonathan],
Shen, Y.L.[Yi-Lin],
Jin, H.X.[Hong-Xia],
Kira, Z.[Zsolt],
Always Be Dreaming:
A New Approach for Data-Free Class-Incremental Learning,
ICCV21(9354-9364)
IEEE DOI
2203
Training, Learning systems, Law, Memory management, Training data,
Benchmark testing,
Recognition and classification
BibRef
Mai, Z.D.[Zhe-Da],
Li, R.[Ruiwen],
Kim, H.W.[Hyun-Woo],
Sanner, S.[Scott],
Supervised Contrastive Replay: Revisiting the Nearest Class Mean
Classifier in Online Class-Incremental Continual Learning,
CLVision21(3584-3594)
IEEE DOI
2109
Training, Performance gain
BibRef
Wu, G.[Guile],
Gong, S.G.[Shao-Gang],
Queen, P.L.[Pan Li],
Striking a Balance between Stability and Plasticity for
Class-Incremental Learning,
ICCV21(1104-1113)
IEEE DOI
2203
Bridges, Computational modeling, Benchmark testing,
Stability analysis, Recognition and classification,
Optimization and learning methods
BibRef
Luo, Z.L.[Zi-Lin],
Liu, Y.Y.[Yao-Yao],
Schiele, B.[Bernt],
Sun, Q.[Qianru],
Class-Incremental Exemplar Compression for Class-Incremental Learning,
CVPR23(11371-11380)
IEEE DOI
2309
BibRef
Earlier: A2, A3, A4, Only:
Adaptive Aggregation Networks for Class-Incremental Learning,
CVPR21(2544-2553)
IEEE DOI
2111
Adaptation models, Adaptive systems,
Network architecture,
Benchmark testing, Stability analysis
BibRef
Yan, S.P.[Shi-Peng],
Xie, J.W.[Jiang-Wei],
He, X.M.[Xu-Ming],
DER: Dynamically Expandable Representation for Class Incremental
Learning,
CVPR21(3013-3022)
IEEE DOI
2111
Visualization, Adaptation models,
Benchmark testing, Feature extraction, Complexity theory
BibRef
Hu, X.T.[Xin-Ting],
Tang, K.H.[Kai-Hua],
Miao, C.Y.[Chun-Yan],
Hua, X.S.[Xian-Sheng],
Zhang, H.W.[Han-Wang],
Distilling Causal Effect of Data in Class-Incremental Learning,
CVPR21(3956-3965)
IEEE DOI
2111
Training, Costs, Streaming media, Benchmark testing,
Reliability
BibRef
van de Ven, G.M.[Gido M.],
Li, Z.[Zhe],
Tolias, A.S.[Andreas S.],
Class-Incremental Learning with Generative Classifiers,
CLVision21(3606-3615)
IEEE DOI
2109
Training, Learning systems, Deep learning,
Monte Carlo methods, Benchmark testing
BibRef
Mittal, S.[Sudhanshu],
Galesso, S.[Silvio],
Brox, T.[Thomas],
Essentials for Class Incremental Learning,
CLVision21(3508-3517)
IEEE DOI
2109
Learning systems, Art, Neural networks,
Training data, Boosting
BibRef
Lechat, A.[Alexis],
Herbin, S.[Stéphane],
Jurie, F.[Frédéric],
Semi-Supervised Class Incremental Learning,
ICPR21(10383-10389)
IEEE DOI
2105
Training, Protocols, Image reconstruction
BibRef
Chang, X.Y.[Xin-Yuan],
Tao, X.Y.[Xiao-Yu],
Hong, X.P.[Xiao-Peng],
Wei, X.[Xing],
Ke, W.[Wei],
Gong, Y.H.[Yi-Hong],
Class-Incremental Learning with Topological Schemas of Memory Spaces,
ICPR21(9719-9726)
IEEE DOI
2105
Multiprotocol label switching, Manifolds, Knowledge engineering,
Adaptation models, Network topology, Neural networks,
Topological Schemas Model
BibRef
Pernici, F.[Federico],
Bruni, M.[Matteo],
Baecchi, C.[Claudio],
Turchini, F.[Francesco],
del Bimbo, A.[Alberto],
Class-incremental Learning with Pre-allocated Fixed Classifiers,
ICPR21(6259-6266)
IEEE DOI
2105
Training, Knowledge engineering, Neural networks, Standards, Faces
BibRef
Lei, C.H.[Cheng-Hsun],
Chen, Y.H.[Yi-Hsin],
Peng, W.H.[Wen-Hsiao],
Chiu, W.C.[Wei-Chen],
Class-Incremental Learning with Rectified Feature-Graph Preservation,
ACCV20(VI:358-374).
Springer DOI
2103
Learn new classes as they arrive.
BibRef
Kim, E.S.,
Kim, J.U.,
Lee, S.,
Moon, S.K.,
Ro, Y.M.,
Class Incremental Learning With Task-Selection,
ICIP20(1846-1850)
IEEE DOI
2011
Task analysis, Learning systems, Image reconstruction,
Feature extraction, Training, Testing, Data models, Deep learning, autoencoder
BibRef
Yu, L.,
Twardowski, B.,
Liu, X.,
Herranz, L.,
Wang, K.,
Cheng, Y.,
Jui, S.,
van de Weijer, J.,
Semantic Drift Compensation for Class-Incremental Learning,
CVPR20(6980-6989)
IEEE DOI
2008
Task analysis, Training, Prototypes, Semantics, Measurement, Neurons
BibRef
Zhao, B.,
Xiao, X.,
Gan, G.,
Zhang, B.,
Xia, S.,
Maintaining Discrimination and Fairness in Class Incremental Learning,
CVPR20(13205-13214)
IEEE DOI
2008
Training, Task analysis, Data models, Error analysis,
Neural networks, Standards
BibRef
Mi, F.,
Kong, L.,
Lin, T.,
Yu, K.,
Faltings, B.,
Generalized Class Incremental Learning,
CLVision20(970-974)
IEEE DOI
2008
Erbium, Training, Data models, Computational modeling,
Probabilistic logic, Machine learning, Task analysis
BibRef
Liu, X.,
Wu, C.,
Menta, M.,
Herranz, L.,
Raducanu, B.,
Bagdanov, A.D.,
Jui, S.,
van de Weijer, J.,
Generative Feature Replay For Class-Incremental Learning,
CLVision20(915-924)
IEEE DOI
2008
Task analysis, Feature extraction,
Image generation, Correlation, Training, Generators
BibRef
Liu, Y.,
Su, Y.,
Liu, A.,
Schiele, B.,
Sun, Q.,
Mnemonics Training: Multi-Class Incremental Learning Without
Forgetting,
CVPR20(12242-12251)
IEEE DOI
2008
Training, Optimization, Data models, Computational modeling,
Generative adversarial networks, Training data
BibRef
Slim, H.[Habib],
Belouadah, E.[Eden],
Popescu, A.[Adrian],
Onchis, D.[Darian],
Dataset Knowledge Transfer for Class-Incremental Learning without
Memory,
WACV22(3311-3320)
IEEE DOI
2202
Training, Deep learning, Design methodology,
Memory management, Neural networks, Semi- and Un- supervised Learning
BibRef
Belouadah, E.[Eden],
Popescu, A.[Adrian],
ScaIL: Classifier Weights Scaling for Class Incremental Learning,
WACV20(1255-1264)
IEEE DOI
2006
BibRef
Earlier:
IL2M: Class Incremental Learning With Dual Memory,
ICCV19(583-592)
IEEE DOI
2004
Tuning, Adaptation models, Training, Feature extraction,
Machine learning, Memory management, Task analysis.
computational complexity, image classification,
inference mechanisms, learning (artificial intelligence),
Computer architecture
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Few-Shot Incremental Learning .