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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-12-26
Cited: 0
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Citations: Bibtex RefMan EndNote GB/T7714
Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting
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Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG. Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 187-202.
@article{title="Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting",
author="Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="2",
pages="187-202",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200380"
}
%0 Journal Article
%T Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting
%A Jiaqi GAO
%A Jingqi LI
%A Hongming SHAN
%A Yanyun QU
%A James Z. WANG
%A Fei-Yue WANG
%A Junping ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
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%P 187-202
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200380
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A1 - Jiaqi GAO
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A1 - Yanyun QU
A1 - James Z. WANG
A1 - Fei-Yue WANG
A1 - Junping ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200380
Abstract: crowd counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks when handling multiple domains: (1) the models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift; (3) it leads to linearly increasing storage overhead, either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new crowd counting task in incremental domain training setting called lifelong crowd counting. Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains. Specifically, we propose a self-distillation learning framework as a benchmark (forget less, count better, or FLCB) for lifelong crowd counting, which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive. A new quantitative metric, normalized Backward Transfer (nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.
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