Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Daniel Sáez-Trigueros"'
Autor:
Andrzej D. Dobrzycki, Ana M. Bernardos, Luca Bergesio, Andrzej Pomirski, Daniel Sáez-Trigueros
Publikováno v:
Mathematics, Vol 12, Iss 1, p 76 (2023)
Accurate human posture classification in images and videos is crucial for automated applications across various fields, including work safety, physical rehabilitation, sports training, or daily assisted living. Recently, multimodal learning methods,
Externí odkaz:
https://doaj.org/article/bd0a25bba3cf424abb4aa3df708250d0
Autor:
María Villa-Monedero, Manuel Gil-Martín, Daniel Sáez-Trigueros, Andrzej Pomirski, Rubén San-Segundo
Publikováno v:
Journal of Imaging, Vol 9, Iss 12, p 262 (2023)
Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes f
Externí odkaz:
https://doaj.org/article/a58ad238ce434ef7b6a8057d0b4ce81a
Autor:
Manuel Gil-Martín, María Villa-Monedero, Andrzej Pomirski, Daniel Sáez-Trigueros, Rubén San-Segundo
Publikováno v:
Sensors, Vol 23, Iss 23, p 9365 (2023)
This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign ph
Externí odkaz:
https://doaj.org/article/305cda5287cd43049dde00d55eb551ef
Autor:
Magdalena Proszewska, Grzegorz Beringer, Daniel Sáez-Trigueros, Thomas Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote
In this paper, we propose GlowVC: a multilingual multi-speaker flow-based model for language-independent text-free voice conversion. We build on Glow-TTS, which provides an architecture that enables use of linguistic features during training without
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75de51f0fc0118eade3ba985f57e6130
http://arxiv.org/abs/2207.01454
http://arxiv.org/abs/2207.01454
Publikováno v:
Neural Networks. 134:86-94
Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be tra