Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Learning a Unified Classifier Incrementally via Rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Closed Access | Times Cited: 881

Showing 26-50 of 881 citing articles:

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Kai Zhu, Yang Cao, Wei Zhai, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), pp. 6797-6806
Open Access | Times Cited: 119
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Zheda Mai, Ruiwen Li, Hyunwoo Kim, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2021), pp. 3584-3594
Open Access | Times Cited: 108
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Kai Zhu, Wei Zhai, Yang Cao, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 9286-9295
Open Access | Times Cited: 104
Constrained Few-shot Class-incremental Learning
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 9047-9057
Open Access | Times Cited: 97
CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning
James Smith, Leonid Karlinsky, Vyshnavi Gutta, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 93
Self-Supervised Models are Continual Learners
Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 9611-9620
Open Access | Times Cited: 81
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Adrian Popescu, Hugo Schindler, et al.
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2023), pp. 3900-3909
Open Access | Times Cited: 62
A survey on few-shot class-incremental learning
Songsong Tian, Lusi Li, Weijun Li, et al.
Neural Networks (2023) Vol. 169, pp. 307-324
Open Access | Times Cited: 60
Deep Class-Incremental Learning: A Survey
Da-Wei Zhou, Qiwei Wang, Zhihong Qi, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 58
Few-Shot Class-Incremental Learning for Medical Time Series Classification
Le Sun, Mingyang Zhang, Benyou Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 28, Iss. 4, pp. 1872-1882
Closed Access | Times Cited: 55
Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions
Jipu Li, Ruyi Huang, Zhuyun Chen, et al.
Advanced Engineering Informatics (2023) Vol. 55, pp. 101883-101883
Closed Access | Times Cited: 48
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, et al.
International Journal of Computer Vision (2024)
Closed Access | Times Cited: 34
Class-Incremental Learning: A Survey
Da-Wei Zhou, Qiwei Wang, Zhihong Qi, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 12, pp. 9851-9873
Open Access | Times Cited: 33
An unknown wafer surface defect detection approach based on Incremental Learning for reliability analysis
Zeyun Zhao, Jia Wang, Qian Tao, et al.
Reliability Engineering & System Safety (2024) Vol. 244, pp. 109966-109966
Closed Access | Times Cited: 29
SAR Target Incremental Recognition Based on Features With Strong Separability
Fei Gao, Lingzhe Kong, Rongling Lang, et al.
IEEE Transactions on Geoscience and Remote Sensing (2024) Vol. 62, pp. 1-13
Open Access | Times Cited: 23
Cross-Domain Class Incremental Broad Network for Continuous Diagnosis of Rotating Machinery Faults Under Variable Operating Conditions
Mingkuan Shi, Chuancang Ding, Shuyuan Chang, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 4, pp. 6356-6368
Closed Access | Times Cited: 19
A Dual-Channel Collaborative Transformer for continual learning
Hao Cai, Yizhe Wang, Yong Luo, et al.
Applied Soft Computing (2025), pp. 112792-112792
Closed Access | Times Cited: 3
Topology-Preserving Class-Incremental Learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, et al.
Lecture notes in computer science (2020), pp. 254-270
Closed Access | Times Cited: 121
Memory-Efficient Incremental Learning Through Feature Adaptation
Ahmet İşcen, Jeffrey Zhang, Svetlana Lazebnik, et al.
Lecture notes in computer science (2020), pp. 699-715
Open Access | Times Cited: 115
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
Umberto Michieli, Pietro Zanuttigh
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), pp. 1114-1124
Open Access | Times Cited: 104
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
James Smith, Yen-Chang Hsu, Jonathan Balloch, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
Open Access | Times Cited: 103
Few-Shot Class-Incremental Learning via Relation Knowledge Distillation
Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 2, pp. 1255-1263
Open Access | Times Cited: 96
On Learning the Geodesic Path for Incremental Learning
Christian Simon, Piotr Koniusz, Mehrtash Harandi
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), pp. 1591-1600
Open Access | Times Cited: 87
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, et al.
Neural Computation (2021), pp. 1-44
Open Access | Times Cited: 86
Generative Feature Replay For Class-Incremental Learning
Xialei Liu, Chenshen Wu, Mikel Menta, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020), pp. 915-924
Open Access | Times Cited: 84

Scroll to top