Scaling up convAtt for sign language recognition
Material type:
ArticleDescription: 1 archivo (624 KB)Subject(s): Online resources: Summary: 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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Biblioteca de la Facultad de Informática | Biblioteca digital | A1380 (Browse shelf(Opens below)) | Link to resource | Recurso en Línea |
Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
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.
Congreso Argentino de Ciencias de la Computación (30mo : 2024 : La Plata, Argentina)