no code implementations • 31 Oct 2022 • Carlos Hurtado, Sarath Shekkizhar, Javier Ruiz-Hidalgo, Antonio Ortega
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces.
no code implementations • 2 Sep 2022 • Elías Abad-Rocamora, Javier Ruiz-Hidalgo
Processing 3D pointclouds with Deep Learning methods is not an easy task.
no code implementations • CVPR 2022 • Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints.
no code implementations • 18 Oct 2021 • David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo, Sarath Shekkizhar
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret.
1 code implementation • 27 Jul 2021 • David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo, Sarath Shekkizhar
Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel.
no code implementations • 23 Sep 2020 • Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks.
1 code implementation • 17 May 2020 • Manuel Rey-Area, Emilio Guirado, Siham Tabik, Javier Ruiz-Hidalgo
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i. e., the network becomes highly biased to the data it has been trained on.
no code implementations • 30 Sep 2019 • Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Geometric 3D scene classification is a very challenging task.
no code implementations • 3 Apr 2019 • Adrià Ciurana, Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Convolutional neural networks (CNNs) have demonstrated their capability to solve different kind of problems in a very huge number of applications.
no code implementations • CVPR 2016 • Eduardo Perez-Pellitero, Jordi Salvador, Javier Ruiz-Hidalgo, Bodo Rosenhahn
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.