An end-to-end face parsing model using channel and spatial attentions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyungjoon | - |
dc.contributor.author | Kim, Hyeonwoo | - |
dc.contributor.author | Cho, Seongkuk | - |
dc.contributor.author | Hwang, Eenjun | - |
dc.date.accessioned | 2022-06-10T21:40:16Z | - |
dc.date.available | 2022-06-10T21:40:16Z | - |
dc.date.created | 2022-06-10 | - |
dc.date.issued | 2022-03-15 | - |
dc.identifier.issn | 0263-2241 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/141901 | - |
dc.description.abstract | Facial image parsing requires accurate extraction of facial components and features, and image segmentation can be used. Recently, various attention mechanisms showed excellent performance in segmentation by extracting features based on spatial and channel relationships for input images. In this paper, we propose a new face parsing technique using an attention block that combines the spatial attention block and the channel attention block to effectively utilize their functions. In this process, we improve the structure of the two blocks to compensate for their weaknesses. The attention block extracts features related to the shape of facial components from spatial relationships and concentrates on more important channels from correlation among channels. We built several segmentation models using the proposed block and compared their performance with well-known segmentation models. Experimental results showed that our combined block-based model can improve the segmentation accuracy by more than 5% in F1 score compared to other models. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | FACIAL LANDMARK DETECTION | - |
dc.title | An end-to-end face parsing model using channel and spatial attentions | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Eenjun | - |
dc.identifier.doi | 10.1016/j.measurement.2022.110807 | - |
dc.identifier.scopusid | 2-s2.0-85124277521 | - |
dc.identifier.wosid | 000796953000005 | - |
dc.identifier.bibliographicCitation | MEASUREMENT, v.191 | - |
dc.relation.isPartOf | MEASUREMENT | - |
dc.citation.title | MEASUREMENT | - |
dc.citation.volume | 191 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | FACIAL LANDMARK DETECTION | - |
dc.subject.keywordAuthor | Face parsing | - |
dc.subject.keywordAuthor | Attention mechanism | - |
dc.subject.keywordAuthor | Image segmentation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.