Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting-Reference-Cited by-同舟云学术

Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting

Published:2023-02 Issue:2 Volume:24 Page:187-202
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Front Inform Technol Electron Eng

Author:

Gao Jiaqi^ORCID,Li Jingqi,Shan Hongming,Qu Yanyun,Wang James Z.,Wang Fei-Yue,Zhang Junping^ORCID

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

https://link.springer.com/content/pdf/10.1631/FITEE.2200380.pdf

Reference68 articles.

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2. Belouadah E, Popescu A, 2019. IL2M: class incremental learning with dual memory. Proc IEEE/CVF Int Conf on Computer Vision, p.583–592. https://doi.org/10.1109/ICCV.2019.00067

3. Boominathan L, Kruthiventi SSS, Babu RV, 2016. Crowd-Net: a deep convolutional network for dense crowd counting. Proc 24th ACM Int Conf on Multimedia, p.640–644. https://doi.org/10.1145/2964284.2967300

4. Cao XK, Wang ZP, Zhao YY, et al., 2018. Scale aggregation network for accurate and efficient crowd counting. Proc 15th European Conf on Computer Vision, p.734–750. https://doi.org/10.1007/978-3-030-01228-1_45

5. Caron M, Misra I, Mairal J, et al., 2020. Unsupervised learning of visual features by contrasting cluster assignments. Proc 34th Int Conf on Neural Information Processing Systems, p.9912–9924.

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