no code implementations • EAMT 2022 • Dimitar Shterionov, Mirella De Sisto, Vincent Vandeghinste, Aoife Brady, Mathieu De Coster, Lorraine Leeson, Josep Blat, Frankie Picron, Marcello Paolo Scipioni, Aditya Parikh, Louis ten Bosh, John O’Flaherty, Joni Dambre, Jorn Rijckaert
The SignON project (www. signon-project. eu) focuses on the research and development of a Sign Language (SL) translation mobile application and an open communications framework.
no code implementations • LREC 2022 • Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gómez, Mathieu De Coster, Dimitar Shterionov, Horacio Saggion
Furthermore, we propose a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages.
no code implementations • 30 Jun 2023 • Mathieu De Coster, Ellen Rushe, Ruth Holmes, Anthony Ventresque, Joni Dambre
However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models.
no code implementations • 7 Feb 2022 • Mathieu De Coster, Dimitar Shterionov, Mieke Van Herreweghe, Joni Dambre
Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics.
1 code implementation • International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL) 2021 • Mathieu De Coster, Karel D'Oosterlinck, Marija Pizurica, Paloma Rabaey, Severine Verlinden, Mieke Van Herreweghe, Joni Dambre
Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.
1 code implementation • Computer Vision and Pattern Recognition Workshops (CVPRW) 2021 • Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain.
Ranked #7 on Sign Language Recognition on AUTSL
no code implementations • LREC 2020 • Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition.