Two-stream lightweight sign language transformer
Despite the recent progress of continuous sign language translation-based video, a variety of deep learning models are difficult to apply to the real-time translation in the limit computing resource. We present the two-stream lightweight sign transformer network model for recognizing and translating...
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| Published in: | Machine vision and applications Vol. 33; no. 5 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2022
Springer Nature B.V |
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| ISSN: | 0932-8092, 1432-1769 |
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| Abstract | Despite the recent progress of continuous sign language translation-based video, a variety of deep learning models are difficult to apply to the real-time translation in the limit computing resource. We present the two-stream lightweight sign transformer network model for recognizing and translating continuous sign language. This lightweight framework can obtain both static spatial information and all body dynamic features of signer, and the transformer-style decoder architecture to real-time translate sentences from the spatio-temporal context around the signer. Additionally its attention mechanism focus on moving hands and mouth of signer, which is often crucial for semantic understanding of sign language. In this paper, we introduce the Chinese sign language corpus of the business scene which consists of 3080 videos of high quality. The Chinese sign language corpus of the business scene has enormous impetuses for further research on the Chinese sign language translation. Experiments are carried out the PHOENIX-Weather 2014T (Camgoz et al, in: Proceedings of IEEE/CVF conference on computer vision and pattern recognition (CVPR 2018), pp 7784–7793, 2018), Chinese Sign Language dataset Huang et al, in: The thirty-second AAAI conference on artificial intelligence (AAAI-18), pp 2257–2264, 2018) and our CSLBS, the proposed model outperforms the state-of-the-art in inference times and accuracy using only raw RGB and RGB difference frames as input. |
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| AbstractList | Despite the recent progress of continuous sign language translation-based video, a variety of deep learning models are difficult to apply to the real-time translation in the limit computing resource. We present the two-stream lightweight sign transformer network model for recognizing and translating continuous sign language. This lightweight framework can obtain both static spatial information and all body dynamic features of signer, and the transformer-style decoder architecture to real-time translate sentences from the spatio-temporal context around the signer. Additionally its attention mechanism focus on moving hands and mouth of signer, which is often crucial for semantic understanding of sign language. In this paper, we introduce the Chinese sign language corpus of the business scene which consists of 3080 videos of high quality. The Chinese sign language corpus of the business scene has enormous impetuses for further research on the Chinese sign language translation. Experiments are carried out the PHOENIX-Weather 2014T (Camgoz et al, in: Proceedings of IEEE/CVF conference on computer vision and pattern recognition (CVPR 2018), pp 7784–7793, 2018), Chinese Sign Language dataset Huang et al, in: The thirty-second AAAI conference on artificial intelligence (AAAI-18), pp 2257–2264, 2018) and our CSLBS, the proposed model outperforms the state-of-the-art in inference times and accuracy using only raw RGB and RGB difference frames as input. |
| ArticleNumber | 79 |
| Author | Chen, Yuming Qin, Xuan Mei, Xue |
| Author_xml | – sequence: 1 givenname: Yuming orcidid: 0000-0001-8253-9373 surname: Chen fullname: Chen, Yuming email: cym@njtech.edu.cn organization: College of Electrical Engineering and Control Science, Nanjing Tech University – sequence: 2 givenname: Xue surname: Mei fullname: Mei, Xue organization: College of Electrical Engineering and Control Science, Nanjing Tech University – sequence: 3 givenname: Xuan surname: Qin fullname: Qin, Xuan organization: China Electronics Technology Group Corp 28th Research Institute |
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