Red ConvAtt: Un Acercamiento Con Bajos Parámetros Para El Reconocimiento De Lengua De Señas
Year: 2024
Type: article
Abstract: Despite recent advances in Large Language Models in text processing, Sign Language Recognition (SLR) remains an unresolved task. This is, in part, due to limitations in the available data. In this paper, we investigate combining 1D convolutions with transformer layers to capture local features and global interactions in a low-parameter SLR model. We experimented using multiple data augmentation and regularization techniques to categorize signs of the French Belgian Sign Language. We achieved a top-1 accuracy of 42.7% and a top-10 accuracy of 81.9% in 600 different signs. This model is competitive with the current state of the art while using a significantly lower number of parameters.
Source: Conicet
Authors: Rios, Gaston Gustavo, Dal Bianco, Pedro, Ronchetti, Franco, Quiroga, Facundo, Ponte Ahón, Santiago Ponte, Stanchi, Oscar Agustín, Hasperué, Waldo
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7052319084
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