Scaling up ConvAtt for Sign Language Recognition

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 au...

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Detalles Bibliográficos
Autores principales: Ríos, Gastón Gustavo, Dal Bianco, Pedro Alejandro, Ronchetti, Franco, Quiroga, Facundo Manuel, Ponte Ahón, Santiago Andrés, Stanchi, Oscar Agustín, Hasperué, Waldo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
Handshape Recognition
Unbalanced Data
Limited Data
Sign Language
Human Motion Prediction
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/176284
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
Descripción
Sumario: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.

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