Ghent University Academic Bibliography

Your filters: cql: author exact F502D5DE-F0ED-11E1-A9DE-61C894A0A6B4 or (type any "bookEditor issueEditor" and edito... cql: subject exact "Languages and Literatures"

Add to list
- Miscellaneous
- open access
Machine translation from signed to spoken languages : state of the art and challenges (01 Apr, 10.1007/s10209-023-00992-1, 2023)

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

(2024) UNIVERSAL ACCESS IN THE INFORMATION SOCIETY.
Add to list
- Journal Article
- A1
- open access
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)

(2024) UNIVERSAL ACCESS IN THE INFORMATION SOCIETY. 23(3). p.1305-1331
Add to list
- Conference Paper
- C1
- open access
Trends and challenges for sign language recognition with machine learning

J. Fink, Mathieu De Coster (UGent) , Joni Dambre (UGent) and B. Frénay

(2023) ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings. p.561-570
Add to list
- Conference Paper
- C1
- open access
SignON sign language translation : progress and challenges

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

(2023) Proceedings of the 24th Annual Conference of the European Association for Machine Translation. p.501-502
Add to list
- Journal Article
- A2
- open access
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
Add to list
- Conference Paper
- C1
- open access
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
Add to list
- Conference Paper
- P1
- open access
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
Add to list
- Conference Paper
- P1
- open access
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
Add to list
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 (01 Apr, 10.1007/s10209-023-00992-1, 2023)

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

Trends and challenges for sign language recognition with machine learning

SignON sign language translation : progress 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

Contents

Support

Contact