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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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
id I19-R120-10915-176284
record_format dspace
spelling I19-R120-10915-1762842025-02-07T20:04:57Z http://sedici.unlp.edu.ar/handle/10915/176284 Scaling up ConvAtt for Sign Language Recognition Ríos, Gastón Gustavo Dal Bianco, Pedro Alejandro Ronchetti, Franco Quiroga, Facundo Manuel Ponte Ahón, Santiago Andrés Stanchi, Oscar Agustín Hasperué, Waldo 2024-10 2024 2025-02-07T17:09:47Z en Ciencias Informáticas Handshape Recognition Unbalanced Data Limited Data Sign Language Human Motion Prediction 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. Red de Universidades con Carreras en Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 145-154
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Handshape Recognition
Unbalanced Data
Limited Data
Sign Language
Human Motion Prediction
spellingShingle Ciencias Informáticas
Handshape Recognition
Unbalanced Data
Limited Data
Sign Language
Human Motion Prediction
Ríos, Gastón Gustavo
Dal Bianco, Pedro Alejandro
Ronchetti, Franco
Quiroga, Facundo Manuel
Ponte Ahón, Santiago Andrés
Stanchi, Oscar Agustín
Hasperué, Waldo
Scaling up ConvAtt for Sign Language Recognition
topic_facet Ciencias Informáticas
Handshape Recognition
Unbalanced Data
Limited Data
Sign Language
Human Motion Prediction
description 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.
format Objeto de conferencia
Objeto de conferencia
author Ríos, Gastón Gustavo
Dal Bianco, Pedro Alejandro
Ronchetti, Franco
Quiroga, Facundo Manuel
Ponte Ahón, Santiago Andrés
Stanchi, Oscar Agustín
Hasperué, Waldo
author_facet Ríos, Gastón Gustavo
Dal Bianco, Pedro Alejandro
Ronchetti, Franco
Quiroga, Facundo Manuel
Ponte Ahón, Santiago Andrés
Stanchi, Oscar Agustín
Hasperué, Waldo
author_sort Ríos, Gastón Gustavo
title Scaling up ConvAtt for Sign Language Recognition
title_short Scaling up ConvAtt for Sign Language Recognition
title_full Scaling up ConvAtt for Sign Language Recognition
title_fullStr Scaling up ConvAtt for Sign Language Recognition
title_full_unstemmed Scaling up ConvAtt for Sign Language Recognition
title_sort scaling up convatt for sign language recognition
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/176284
work_keys_str_mv AT riosgastongustavo scalingupconvattforsignlanguagerecognition
AT dalbiancopedroalejandro scalingupconvattforsignlanguagerecognition
AT ronchettifranco scalingupconvattforsignlanguagerecognition
AT quirogafacundomanuel scalingupconvattforsignlanguagerecognition
AT ponteahonsantiagoandres scalingupconvattforsignlanguagerecognition
AT stanchioscaragustin scalingupconvattforsignlanguagerecognition
AT hasperuewaldo scalingupconvattforsignlanguagerecognition
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