Ghent University Academic Bibliography

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Machine translation from signed to spoken languages : state of the art and challenges

Mathieu De Coster (UGent) , Dimitar Shterionov, Mieke Van Herreweghe (UGent) and Joni Dambre (UGent)

(2023) UNIVERSAL ACCESS IN THE INFORMATION SOCIETY. p.1-27
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BeCoS corpus : Belgian Covid-19 Sign language corpus : a corpus for training sign language recognition and translation

Vincent Vandeghinste, Bob Van Dyck, Mathieu De Coster (UGent) , Maud Goddefroy and Joni Dambre (UGent)

(2022) COMPUTATIONAL LINGUISTICS IN THE NETHERLANDS JOURNAL. 12. p.7-17
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Sign language translation : ongoing development, challenges and innovations in the SignON project

Dimitar Shterionov, Mirella De Sisto, Vincent Vandeghinste, Aoife Brady, Mathieu De Coster (UGent) , Lorraine Leeson, Josep Blat, Frankie Picron, Marcello Scipioni, Aditya Parikh, et al.

(2022) Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. p.325-326
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Isolated sign recognition from RGB video using pose flow and self-attention

Mathieu De Coster (UGent) , Mieke Van Herreweghe (UGent) and Joni Dambre (UGent)

(2021) 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). In IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops p.3441-3450
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Sign language recognition with transformer networks

Mathieu De Coster (UGent) , Mieke Van Herreweghe (UGent) and Joni Dambre (UGent)

(2020) PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020). p.6018-6024
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Towards automatic sign language corpus annotation using deep learning

Mathieu De Coster (UGent) , Mieke Van Herreweghe (UGent) and Joni Dambre (UGent)

(2019) 6th Workshop on Sign Language Translation and Avatar Technology (SLTAT).

Academic Bibliography

Machine translation from signed to spoken languages : state of the art and challenges

BeCoS corpus : Belgian Covid-19 Sign language corpus : a corpus for training sign language recognition and translation

Sign language translation : ongoing development, challenges and innovations in the SignON project

Isolated sign recognition from RGB video using pose flow and self-attention

Sign language recognition with transformer networks

Towards automatic sign language corpus annotation using deep learning

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