Friday, June 3, 2016, 10:00 am to 11:00 am, room F107, INRIA Montbonnot
Seminar by Xavier Alameda-Pineda, University of Trento, Italy
Abstract: Matrix completion is a generic framework aiming to recover a matrix from a limited number of (possibly noisy) entries. In this context, low-rank regularizers are often imposed so as to find matrix estimators that are robust to noise and outliers. In this talk I will discuss three recent advances on matrix completion, developed to solve three different vision applications. First, coupled matrix completion to solve joint head and body pose estimation. Second, non-linear matrix completion to recognize emotions from abstract paintings. Third, self-adaptive matrix completion for remote heart-rate estimation from videos.
The talk is based on two papers accepted at CVPR’16.
