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General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds With One Stone

文献类型：期刊论文

作者Bao, Zenghao1,2; Tan, Zichang3; Li, Jun1,2; Wan, Jun1,2,4; Ma, Xibo1,2; Lei, Zhen1,2,5
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:6155-6167
ISSN号1057-7149
关键词General age estimation long-tailed age estimation class-wise mean absolute error
DOI10.1109/TIP.2023.3327540
通讯作者Wan, Jun(jun.wan@ia.ac.cn)
英文摘要Facial age estimation has received a lot of attention for its diverse application scenarios. Most existing studies treat each sample equally and aim to reduce the average estimation error for the entire dataset, which can be summarized as General Age Estimation. However, due to the long-tailed distribution prevalent in the dataset, treating all samples equally will inevitably bias the model toward the head classes (usually the adult with a majority of samples). Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation. However, Long-tailed Age Estimation usually faces a performance trade-off, i.e., achieving improvement in tail classes by sacrificing the head classes. In this paper, our goal is to design a unified framework to perform well on both tasks, killing two birds with one stone. To this end, we propose a simple, effective, and flexible training paradigm named GLAE, which is two-fold. First, we propose Feature Rearrangement (FR) and Pixel-level Auxiliary learning (PA) for better feature utilization to improve the overall age estimation performance. Second, we propose Adaptive Routing (AR) for selecting the appropriate classifier to improve performance in the tail classes while maintaining the head classes. Moreover, we introduce a new metric, named Class-wise Mean Absolute Error (CMAE), to equally evaluate the performance of all classes. Our GLAE provides a surprising improvement on Morph II, reaching the lowest MAE and CMAE of 1.14 and 1.27 years, respectively. Compared to the previous best method, MAE dropped by up to 34%, which is an unprecedented improvement, and for the first time, MAE is close to 1 year old. Extensive experiments on other age benchmark datasets, including CACD, MIVIA, and Chalearn LAP 2015, also indicate that GLAE outperforms the state-of-the-art approaches significantly.
WOS关键词RECOGNITION
资助项目National Key Research and Development Plan[2021YFE0205700] ; Beijing Natural Science Foundation[JQ23016] ; External Cooperation Key Project of Chinese Academy Sciences[173211KYSB20200002] ; Chinese National Natural Science Foundation Projects[62276254] ; Chinese National Natural Science Foundation Projects[82090051] ; Science and Technology Development Fund of Macau[0070/2020/AMJ] ; Guangdong Provincial Key Research and Development Program[2019B010148001] ; China Computer Federation (CCF)-Zhipu AI Large Model[202219] ; Open Research Projects of Zhejiang Laboratory[2021KH0AB07] ; InnoHK Program
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:001105231300003
资助机构National Key Research and Development Plan ; Beijing Natural Science Foundation ; External Cooperation Key Project of Chinese Academy Sciences ; Chinese National Natural Science Foundation Projects ; Science and Technology Development Fund of Macau ; Guangdong Provincial Key Research and Development Program ; China Computer Federation (CCF)-Zhipu AI Large Model ; Open Research Projects of Zhejiang Laboratory ; InnoHK Program
源URL[http://ir.ia.ac.cn/handle/173211/55222]  
专题多模态人工智能系统全国重点实验室
通讯作者Wan, Jun
作者单位1.Chinese Acad Sci CASIA, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Baidu Res, Inst Deep Learning, Beijing 100085, Peoples R China
4.Macau Univ Sci & Technol, Sch Comp Sci & Engn, Taipa, Macau, Peoples R China
5.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Bao, Zenghao,Tan, Zichang,Li, Jun,et al. General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds With One Stone[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:6155-6167.
APA Bao, Zenghao,Tan, Zichang,Li, Jun,Wan, Jun,Ma, Xibo,&Lei, Zhen.(2023).General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds With One Stone.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,6155-6167.
MLA Bao, Zenghao,et al."General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds With One Stone".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):6155-6167.

入库方式： OAI收割

来源：自动化研究所

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