Knowledge Commons of Institute of Automation,CAS
ATF: An Alternating Training Framework for Weakly Supervised Face Alignment | |
Lan, Xing1,2; Hu, Qinghao2; Cheng, Jian1,2 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
2023 | |
卷号 | 25页码:1798-1809 |
通讯作者 | Cheng, Jian(jcheng@nlpr.ia.ac.cn) |
摘要 | In recent years, various face-landmark datasets have been published. Intuitively, it is significant to integrate multiple labeled datasets to achieve higher performance. Due to the different annotation schemes of datasets, it is hard to directly train models using them together. Although numerous efforts have been made in the joint use of datasets, there remain three shortages in previous methods, i.e., additional computation, limitation of the markups scheme, and limited support for the regression method. To solve the above issues, we proposed a novel Alternating Training Framework (ATF), which leverages the similarity and diversity across multiple datasets for a more robust detector. ATF mainly contains two sub-modules: Alternating Training with Decreasing Proportions (ATDP) and Mixed Branch Loss (L-MB). In particular, ATDP trains multiple datasets simultaneously via a weakly supervised way to take advantage of the diversity among them, and L-MB utilizes similar landmark pairs to constrain different branches of the corresponding datasets. Besides, we extend the framework to easily handle three situations: single target detector, joint detector, and novel detector. Extensive experiments demonstrate the effectiveness of our framework for both heatmap-based and direct coordinate regression. Moreover, we have achieved a joint detector that outperforms state-of-the-art methods on each benchmark. |
关键词 | Face alignment multi-task learning weakly supervised |
DOI | 10.1109/TMM.2022.3164798 |
关键词[WOS] | NETWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2021ZD0201504] ; National Natural Science Foundation of China[62106267] ; Jiangsu Key Research and Development Plan[BE2021012-2] ; Jiangsu Leading Technology Basic Research Project[BK20192004] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Jiangsu Key Research and Development Plan ; Jiangsu Leading Technology Basic Research Project |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:001007432100016 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53700 |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Cheng, Jian |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci CASIA, Natl Lab Pattern Recognit NLPR, Inst Automat, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Lan, Xing,Hu, Qinghao,Cheng, Jian. ATF: An Alternating Training Framework for Weakly Supervised Face Alignment[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:1798-1809. |
APA | Lan, Xing,Hu, Qinghao,&Cheng, Jian.(2023).ATF: An Alternating Training Framework for Weakly Supervised Face Alignment.IEEE TRANSACTIONS ON MULTIMEDIA,25,1798-1809. |
MLA | Lan, Xing,et al."ATF: An Alternating Training Framework for Weakly Supervised Face Alignment".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):1798-1809. |
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