Scaling up convAtt for sign language recognition
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| Otros autores o Colaboradores: | , , , , , |
| Formato: | Capítulo de libro |
| Lengua: | inglés |
| Temas: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/176284 Consultar en el Cátalogo |
| 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. |
| Notas: | Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) |
| Descripción Física: | 1 archivo (624 KB) |