1 code implementation • 18 Apr 2023 • Túlio Chiodi, Arthur dos Santos, Pedro Martins, Bruno Masiero
In this paper, we introduce a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches.
no code implementations • 7 Sep 2022 • Sérgio M. Rebelo, Mariana Seiça, Pedro Martins, João Bicker, Penousal Machado
We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter.
no code implementations • 28 May 2022 • Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra, Thibaut Vidal
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth.
1 code implementation • Scientific Reports 2022 • Mauro Pinto, Tiago Coelho, Adriana Leal, Fábio Lopes, António Dourado, Pedro Martins, César Teixeira
We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients.
no code implementations • 27 Jan 2021 • Pedro Martins, Filipa Alarcão Martins
On a different direction, using the second index, the strong target members should characterize relevant consumers of information in the network, which may include fake news' regular collectors.
Social and Information Networks 68R10, 90B18
no code implementations • 27 Jun 2017 • João M. Cunha, João Gonçalves, Pedro Martins, Penousal Machado, Amílcar Cardoso
A descriptive approach for automatic generation of visual blends is presented.
no code implementations • CVPR 2015 • Rui Caseiro, Joao F. Henriques, Pedro Martins, Jorge Batista
In this case, the source/target domains are represented in the form of subspaces, which are treated as points on the Grassmann manifold.
no code implementations • NeurIPS 2014 • João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista
In many datasets, the samples are related by a known image transformation, such as rotation, or a repeatable non-rigid deformation.
no code implementations • CVPR 2014 • Pedro Martins, Rui Caseiro, Jorge Batista
This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images.
9 code implementations • 30 Apr 2014 • João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista
Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers.
no code implementations • CVPR 2013 • Rui Caseiro, Pedro Martins, Joao F. Henriques, Fatima Silva Leite, Jorge Batista
In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27].