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Authors: Natsuki Takayama 1 ; Gibran Benitez-Garcia 1 and Hiroki Takahashi 2 ; 1

Affiliations: 1 Graduate School of Informatics and Engineering, the University of Electro-Communications, Japan ; 2 Artificial Intelligence Exploration Research Center, the University of Electro-Communications, Japan

Keyword(s): Monotonic Attention, Neural Networks, Skeleton-ased Sign Language Recognition.

Abstract: Sequence-to-sequence models have been successfully applied to improve continuous sign language word recognition in recent years. Although various methods for continuous sign language word recognition have been proposed, these methods assume offline recognition and lack further investigation in online and streaming situations. In this study, skeleton-based continuous sign language word recognition for online situations was investigated. A combination of spatial-temporal graph convolutional networks and recurrent neural networks with soft attention was employed as the base model. Further, three types of monotonic attention techniques were applied to extend the base model for online recognition. The monotonic attention included hard monotonic attention, monotonic chunkwise attention, and monotonic infinite lookback attention. The performance of the proposed models was evaluated in offline and online recognition settings. A conventional Japanese sign language video dataset, including 275 types of isolated word videos and 113 types of sentence videos, was utilized to evaluate the proposed models. The results showed that the effectiveness of monotonic attention to online continuous sign language word recognition. (More)

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Paper citation in several formats:
Takayama, N.; Benitez-Garcia, G. and Takahashi, H. (2022). Skeleton-based Online Sign Language Recognition using Monotonic Attention. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 601-608. DOI: 10.5220/0010899400003124

@conference{visapp22,
author={Natsuki Takayama. and Gibran Benitez{-}Garcia. and Hiroki Takahashi.},
title={Skeleton-based Online Sign Language Recognition using Monotonic Attention},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={601-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010899400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Skeleton-based Online Sign Language Recognition using Monotonic Attention
SN - 978-989-758-555-5
IS - 2184-4321
AU - Takayama, N.
AU - Benitez-Garcia, G.
AU - Takahashi, H.
PY - 2022
SP - 601
EP - 608
DO - 10.5220/0010899400003124
PB - SciTePress

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