no code implementations • 26 Oct 2023 • Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini, Alejandro Rosete
The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks.
no code implementations • Journal of Computer Science and Technology (JCST) 2016 • Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning.
Ranked #3 on Hand Gesture Recognition on LSA16
no code implementations • Frontiers of Computer Science 2015 • Facundo Manuel Quiroga, Franco Ronchetti, Laura Lanzarini, Cesar Eestrebou
Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets.
Ranked #1 on Skeleton Based Action Recognition on MSR Action3D
no code implementations • 26 Oct 2023 • Facundo Manuel Quiroga, Jordina Torrents-Barrena, Laura Cristina Lanzarini, Domenec Puig-Valls
We propose measures to quantify the invariance of neural networks in terms of their internal representation.
no code implementations • 26 Oct 2023 • Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini, Alejandro Rosete
The model employs a bag-of-words approach in all classification steps, to explore the hypothesis that ordering is not essential for recognition.
no code implementations • 12 Oct 2023 • Facundo Manuel Quiroga, Franco Ronchetti, Laura Lanzarini, Aurelio Fernandez-Bariviera
In the case of data augmented networks, we also analyze which layers help the network to encode the rotational invariance, which is important for understanding its limitations and how to best retrain a network with data augmentation to achieve invariance to rotation.