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Heterogeneous attention based transformer for sign language translation

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posted on 2023-06-29, 16:02 authored by Hao Zhang, Yixiang Sun, Zenghui Liu, Qiyuan Liu, Xiyao Liu, Ming Jiang, Gerald SchaeferGerald Schaefer, Hui FangHui Fang

Sign language translation (SLT) has attracted significant interest both from research and industry, enabling convenient communications with the deaf-mute community. While recent transformer-based models have shown improved sign translation performance, it is still under-explored how to design an efficient transformer-based deep network architecture that effectively extracts joint visual-text features by exploiting multi-level spatial and temporal contextual information. In this paper, we propose heterogeneous attention based transformer(HAT), a novel SLT model to generate attentions from diverse spatial and temporal contextual levels. Specifically, the proposed light dual-stream sparse attention-based module yields more effective visual-text representations compared to conventional transformers. Extensive experiments demonstrate that our HAT achieves state-of-the-art performance on the challenging PHOENIX2014T benchmark dataset with a BLEU-4 score of 25.33 on the test set.

Funding

Natural Science Foundation of Hunan Province, China [2022GK5002,2020JJ4746]

Special Foundation for Distinguished Young Scientists of Changsha [kq2209003]

Guangxi Key Laboratory of Cryptography and Information Security [GCIS202113]

111 Project [No.D23006]

History

School

  • Science

Department

  • Computer Science

Published in

Applied Soft Computing

Volume

144

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-06-06

Publication date

2023-06-14

Copyright date

2023

ISSN

1568-4946

eISSN

1872-9681

Language

  • en

Depositor

Dr Hui Fang. Deposit date: 9 June 2023

Article number

110526

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