Team

Niklas Penzel, Clemens-Alexander Brust, Paul Bodesheim

Motivation

Although labeled data lies at the very core of most computer vision systems, obtaining labeled data that is useful and reliable is commonly a crucial problem. To reduce the amount of manual labeling, active learning techniques aim at explicitly picking samples that are actually worth being labeled with respect to the problem on hand. In this area of research, we are interested in modeling the “worthiness” of an unlabeled sample and to apply our algorithms to human-in-the-loop recognition systems.