Scaling up convAtt for sign language recognition

Título:

Scaling up convAtt for sign language recognition

Autor:

Ríos, Gastón Gustavo

Colaboradores:

; Dal Bianco, Pedro Alejandro; Ronchetti, Franco; Quiroga, Facundo Manuel; Ponte Ahón, Santiago Andrés; Stanchi, Oscar; Hasperué, Waldo

Temas:

TECNOLOGÍAS PARA PERSONAS CON DISCAPACIDADES

En:

Congreso Argentino de Ciencias de la Computación (30mo : 2024 : La Plata, Argentina)

Resumen:

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.

URL/DOI:

http://sedici.unlp.edu.ar/handle/10915/176284

Palabras clave:

lenguaje de señas

Medio:

Soporte electrónico

Tipo de documento:

Artículo

Descripción física:

1 archivo (624 KB)

Idioma:

Inglés

Publicación:

, 2024

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