CASIA OpenIR  > 智能感知与计算
Contrastive attention network with dense field estimation for face completion
Ma, Xin1,2,3,4; Zhou, Xiaoqiang2,3,4,6; Huang, Huaibo1,2,3,4; Jia, Gengyun1,2,3,4; Chai, Zhenhua5; Wei, Xiaolin5
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2022-04-01
Volume124Pages:13
Abstract

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.

KeywordFace completion Unsupervised learning Attention mechanism 3D Face analysis
DOI10.1016/j.patcog.2021.108465
WOS KeywordADVERSARIAL NETWORK ; IMAGE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundatio of China[62006228]
Funding OrganizationNational Natural Science Foundatio of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000736980400001
PublisherELSEVIER SCI LTD
Sub direction classification图像视频处理与分析
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47140
Collection智能感知与计算
Corresponding AuthorMa, Xin
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.NLPR, Beijing, Peoples R China
3.CEBSIT, Beijing, Peoples R China
4.CASIA, CRIPAC, Beijing, Peoples R China
5.Visual Intelligence Dept, Meituan, Peoples R China
6.Univ Sci & Technol China, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
1-s2.0-S003132032100(3539KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ma, Xin]'s Articles
[Zhou, Xiaoqiang]'s Articles
[Huang, Huaibo]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ma, Xin]'s Articles
[Zhou, Xiaoqiang]'s Articles
[Huang, Huaibo]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ma, Xin]'s Articles
[Zhou, Xiaoqiang]'s Articles
[Huang, Huaibo]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1-s2.0-S0031320321006415-main.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.