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Divergence-Driven Consistency Training for Semi-Supervised Facial Age Estimation

文献类型：期刊论文

作者Bao, Zenghao6,7; Tan, Zichang4,5; Wan, Jun3,6,7; Ma, Xibo6,7; Guo, Guodong2; Lei, Zhen1,6,7
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2023
卷号18页码:221-232
ISSN号1556-6013
关键词Facial age estimation semi-supervised efficient sample selection identity consistency
DOI10.1109/TIFS.2022.3218431
通讯作者Wan, Jun(jun.wan@ia.ac.cn) ; Ma, Xibo(xibo.ma@nlpr.ia.ac.cn)
英文摘要Facial age estimation has attracted considerable attention owing to its great potential in applications. However, it still falls short of reliable age estimation due to the lack of sufficient training data with accurate age labels. Using conventional semi-supervised methods to exploit unlabeled data appears to be a good solution, but it does not yield sufficient performance gains while significantly increasing training time. Therefore, to tackle these problems, we present a Divergence-driven Consistency Training (DCT) method for enhancing both efficiency and performance in this paper. Following the idea of pseudo-labeling and consistency regularization, we assign pseudo labels predicted by the teacher model to unlabeled samples and then train the student model on labeled and unlabeled samples based on consistency regularization. Based on this, we propose two main promotions. The first is the Efficient Sample Selection (ESS) strategy, which is based on the Divergence Score to select effective samples from massive unlabeled images to reduce the training time and improve efficiency. The second is Identity Consistency (IC) regularization as the additional loss function, which introduces a high dependency of aging traits on a person. Moreover, we propose Local Prediction (LP), which is a plug-and-play component, to capture local semantics. Extensive experiments on multiple age benchmark datasets, including CACD, Morph II, MIVIA, and Chalearn LAP 2015, indicate DCT outperforms the state-of-the-art approaches significantly.
WOS关键词RECOGNITION
资助项目National Key Research and Development Plan[2021YFE0205700] ; External Cooperation Key Project of Chinese Academy Sciences[173211KYSB20200002] ; Chinese National Natural Science Foundation[61876179] ; Chinese National Natural Science Foundation[61961160704] ; Chinese National Natural Science Foundation[62276254] ; Chinese National Natural Science Foundation[62176256] ; Chinese National Natural Science Foundation[62106264] ; Science and Technology Development Fund of Macau[0070/2020/AMJ] ; Zhejiang Laboratory[2021KH0AB01]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000905076700013
资助机构National Key Research and Development Plan ; External Cooperation Key Project of Chinese Academy Sciences ; Chinese National Natural Science Foundation ; Science and Technology Development Fund of Macau ; Zhejiang Laboratory
源URL[http://ir.ia.ac.cn/handle/173211/51129]  
专题多模态人工智能系统全国重点实验室
通讯作者Wan, Jun; Ma, Xibo
作者单位1.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China
2.Ant Grp, Beijing 100026, Peoples R China
3.Macau Univ Sci & Technol MUST, Fac Innovat Engn, Macau, Peoples R China
4.Natl Engn Lab Deep Learning Technol & Applicat, Beijing 100101, Peoples R China
5.Baidu Res, Inst Deep Learning, Beijing 100085, Peoples R China
6.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
7.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Bao, Zenghao,Tan, Zichang,Wan, Jun,et al. Divergence-Driven Consistency Training for Semi-Supervised Facial Age Estimation[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2023,18:221-232.
APA Bao, Zenghao,Tan, Zichang,Wan, Jun,Ma, Xibo,Guo, Guodong,&Lei, Zhen.(2023).Divergence-Driven Consistency Training for Semi-Supervised Facial Age Estimation.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,18,221-232.
MLA Bao, Zenghao,et al."Divergence-Driven Consistency Training for Semi-Supervised Facial Age Estimation".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 18(2023):221-232.

入库方式： OAI收割

来源：自动化研究所

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