Sign Language Segmentation Using a Transformer-based Approach

Pérez Villegas, Luis Francisco. (2022). Sign Language Segmentation Using a Transformer-based Approach Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

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Título Sign Language Segmentation Using a Transformer-based Approach
Autor(es) Pérez Villegas, Luis Francisco
Abstract Continuous Sign Language Recognition (CSLR), predicting the meaning of the signs in sign language sentences, is one of the current challenges in translation between sign and spoken languages, that would benefit people with hearing impairment. An important limitation of this research field is the lack of annotated datasets, which could be minimized with Sign Segmentation approaches by automating the costly task of manually annotating the beginning and ending of each sign. The goal of this paper is to study the performance of an architecture which combines I3D CNN extracted features with a transformer-based model called ASFormer which was created specifically for Action Segmentation task. In our approach ASFormer, instead of separating actions in motions is separating signs in a signed speech. Several ablation studies are performed, and it is shown that ASFormer is suitable for segmenting the signs, with a performance near the ones of the state-of-the-art models, confirming the promising benefits of using attention-based approaches in this field.
Notas adicionales Trabajo de Fin de Máster Universitario en Investigación en Inteligencia Artificial. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Santos Martín, Olga C.
Fecha 2022-09-01
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IIA-Lfperez
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IIA-Lfperez
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Thu, 14 Sep 2023, 21:03:29 CET