Self-sufficient framework for continuous sign language recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Youngjoon | - |
dc.contributor.author | Oh, Youngtaek | - |
dc.contributor.author | Cho, Jae Won | - |
dc.contributor.author | Kim, Myungchul | - |
dc.contributor.author | Kim, Dong Jin | - |
dc.contributor.author | Kweon, In So | - |
dc.contributor.author | Chung, Joon Son | - |
dc.date.accessioned | 2023-09-11T01:51:40Z | - |
dc.date.available | 2023-09-11T01:51:40Z | - |
dc.date.created | 2023-07-20 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190373 | - |
dc.description.abstract | The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this end, we propose (1) Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations, and (2) Dense Pseudo-Label Refinement (DPLR) which propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence. We demonstrate that our model achieves state-of-the-art performance among RGB-based methods on large-scale CSLR benchmarks, PHOENIX-2014 and PHOENIX-2014-T, while showing comparable results with better efficiency when compared to other approaches that use multi-modality or extra annotations. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Signal Processing Society | - |
dc.title | Self-sufficient framework for continuous sign language recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Dong Jin | - |
dc.identifier.doi | 10.1109/ICASSP49357.2023.10095732 | - |
dc.identifier.bibliographicCitation | IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1 - 5 | - |
dc.relation.isPartOf | IEEE International Conference on Acoustics, Speech and Signal Processing | - |
dc.citation.title | IEEE International Conference on Acoustics, Speech and Signal Processing | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 5 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceeding | - |
dc.description.journalClass | 3 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10095732 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.