ESANN 2023 special session - Machine Learning Applied to Sign Language
A special session on “Machine Learning Applied to Sign Language” will be held at the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023). This session is co-organized by IDLab-AIRO.
Deep learning has led to spectacular advances in many fields dealing with unstructured data such as computer vision, natural language processing, and data generation. Recently, sign languages have drawn the attention of machine learning practitioners as sign language recognition, translation, and synthesis raise interesting technical challenges and have a clear societal impact. The overarching domain of sign language processing is related to computer vision, natural language processing, computer graphics, and human-computer interaction. It brings together computer scientists and linguists to tackle interdisciplinary problems. This special session aims to highlight recent advances made in sign language recognition, translation, and synthesis, as well as new datasets.
Topics of interest include, but are not limited to:
- Sign language recognition models
- Sign language translation models (from signed to spoken languages and vice versa)
- Sign language synthesis and virtual signing avatars
- Data collection efforts related to sign language processing
Prospective authors must submit their paper through the ESANN portal following the instructions provided here. Author guidelines are available here. Each paper will undergo a peer reviewing process for its acceptance. Authors should send an e-mail with the tentative title of their contribution to the special session organizers as soon as possible.
- Paper submission deadline: 2 May 2023
- Notification of acceptance: 16 June 2023
- The ESANN 2023 conference: 4-5 October 2023
This special session is organized by a team of researchers from IDLab-AIRO (UGent) and the University of Namur.
- Joni Dambre (UGent)
- Mathieu De Coster (UGent)
- Jérôme Fink (UNamur)
- Benoît Frénay (UNamur)