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ATF: An Alternating Training Framework for Weakly Supervised Face Alignment

文献类型：期刊论文

作者Lan, Xing1,2; Hu, Qinghao2; Cheng, Jian1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2023
卷号25页码:1798-1809
关键词Face alignment multi-task learning weakly supervised
ISSN号1520-9210
DOI10.1109/TMM.2022.3164798
通讯作者Cheng, Jian(jcheng@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词NETWORK
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]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001007432100016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/53699]  
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
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.

入库方式： OAI收割

来源：自动化研究所

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