PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

C. Rupprecht, I. Laina, R. DiPietro, M. Baust, F. Tombari, N. Navab, G. D. Hager
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
International Conference on Computer Vision (ICCV 2017), Venice, Italy, October 2017 (bib)

Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In next-frame or future prediction, for example, many distinct outcomes are equally valid. In other cases, uncertainty arises from the way data is labeled. For example, in object detection, many objects of interest often go unlabeled, and in human pose estimation, occluded joints are often labeled with ambiguous values. In this work we focus on a principled approach for handling such scenarios. In particular, we propose a framework for reformulating existing single-prediction models as multiple hypothesis prediction (MHP) models, and we propose an associated meta loss and optimization procedure to train them. To demonstrate our approach, we consider three diverse applications: human pose estimation, future prediction and image classification. We find that MHP models outperform their single-hypothesis counterparts in all cases, and that MHP models simultaneously expose valuable insights into the variability of predictions.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif