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Contrastive attention network with dense field estimation for face completion

文献类型：期刊论文

作者Ma, Xin1,2,5,6; Zhou, Xiaoqiang1,2,4,6; Huang, Huaibo1,2,5,6; Jia, Gengyun1,2,5,6; Chai, Zhenhua3; Wei, Xiaolin3
刊名PATTERN RECOGNITION
出版日期2022-04-01
卷号124页码:13
关键词Face completion Unsupervised learning Attention mechanism 3D Face analysis
ISSN号0031-3203
DOI10.1016/j.patcog.2021.108465
通讯作者Ma, Xin(xin.ma@cripac.ia.ac.cn)
英文摘要Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations that play an important role in meeting the challenges of sophisticated learning tasks. Specifically, various kinds of masks are often presented in face images in the wild, forming complex patterns, especially in this hard period of COVID19. It's difficult for encoders to capture such powerful representations under this complex situation. To address this challenge, we propose a self-supervised Siamese inference network to improve the generalization and robustness of encoders. It can encode contextual semantics from full-resolution images and obtain more discriminative representations. To deal with geometric variations of face images, a dense correspondence field is integrated into the network. We further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine the restored and known regions in an adaptive manner. This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images. Extensive experiments clearly demonstrate that the proposed approach not only achieves more appealing results compared with state-of-the-art methods but also improves the performance of masked face recognition dramatically. (c) 2021 Elsevier Ltd. All rights reserved.
WOS关键词ADVERSARIAL NETWORK ; IMAGE
资助项目National Natural Science Foundatio of China[62006228]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000736980400001
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundatio of China
源URL[http://ir.ia.ac.cn/handle/173211/47139]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Ma, Xin
作者单位1.CEBSIT, Beijing, Peoples R China
2.CASIA, CRIPAC, Beijing, Peoples R China
3.Visual Intelligence Dept, Meituan, Peoples R China
4.Univ Sci & Technol China, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.NLPR, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Xin,Zhou, Xiaoqiang,Huang, Huaibo,et al. Contrastive attention network with dense field estimation for face completion[J]. PATTERN RECOGNITION,2022,124:13.
APA Ma, Xin,Zhou, Xiaoqiang,Huang, Huaibo,Jia, Gengyun,Chai, Zhenhua,&Wei, Xiaolin.(2022).Contrastive attention network with dense field estimation for face completion.PATTERN RECOGNITION,124,13.
MLA Ma, Xin,et al."Contrastive attention network with dense field estimation for face completion".PATTERN RECOGNITION 124(2022):13.

入库方式： OAI收割

来源：自动化研究所

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