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 • 22 Mar 2015 • Joao Carreira, Sara Vicente, Lourdes Agapito, Jorge Batista
In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image.
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 • Sara Vicente, Joao Carreira, Lourdes Agapito, Jorge Batista
We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations.
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].