no code implementations • 19 Jan 2024 • Miquel Miró-Nicolau, Antoni Jaume-i-Capó, Gabriel Moyà-Alcover
We applied our benchmark to assess the existing fidelity metrics in two different experiments, each using public datasets comprising 52, 000 images.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 11 Feb 2023 • Miquel Miró-Nicolau, Antoni Jaume-i-Capó, Gabriel Moyà-Alcover
With the increased usage of artificial intelligence (AI), it is imperative to understand how these models work internally.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 9 Oct 2020 • Nataša Petrović, Gabriel Moyà-Alcover, Antoni Jaume-i-Capó, Manuel González-Hidalgo
We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models.
1 code implementation • 3 Aug 2020 • Miquel Miró-Nicolau, Biel Moyà-Alcover, Manuel Gonzàlez-Hidalgo, Antoni Jaume-i-Capó
Finally, we obtain a concave point from each region based on the analysis of the relative position of their neighbourhood We experimentally demonstrated that a better concave points detection implies a better cluster division.
no code implementations • 10 May 2018 • Gabriel Moyà, Antoni Jaume-i-Capó, Javier Varona
Most of the current research in computer vision is focused on working with single images without taking in account temporal information.
no code implementations • 29 Sep 2016 • Gabriel Moyà-Alcover, Ahmed Elgammal, Antoni Jaume-i-Capó, Javier Varona
In order to unify all the device channel cues, a new probabilistic depth data model is also proposed where we show how handle the inaccurate data to improve foreground segmentation.