TY  - GEN
T1  - Scaling up convAtt for sign language recognition
A1  - Ríos, Gastón Gustavo
A2  - Dal Bianco, Pedro Alejandro
A2  - Ronchetti, Franco
A2  - Quiroga, Facundo Manuel
A2  - Ponte Ahón, Santiago Andrés
A2  - Stanchi, Oscar
A2  - Hasperué, Waldo
LA  - English
UL  - http://vufind10-pruebas.sigbunlp.bibliotecas.unlp.edu.ar/Record/dif.57982
AB  - Sign language is crucial for communication within the deaf community, making Sign Language Recognition (SLR) essential for bridging the gap between signers and non-signers. However, SLR models often face challenges due to limited data availability and quality. This paper investigates various data augmentation and regularization techniques to enhance the performance of a lightweight SLR model. We focus on recognizing signs from the French Belgian Sign Language using a novel model architecture that integrates convolutional, channel attention, and selfattention layers. Our experiments demonstrate the effectiveness of these techniques, achieving a top-1 accuracy of 49.99% and a top-10 accuracy of 83.19% across 600 distinct signs.
NO  - Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
KW  - TECNOLOGÍAS PARA PERSONAS CON DISCAPACIDADES
KW  - lenguaje de señas
ER  -