2016/12/01 by Christian Rupprecht, Iro Laina, Rupprecht, Christian +11 · 20 citations
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning and Data Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.00197
ICCV 2017
openalex publication_date 2016/12/01 · arxiv created 2017/08/22 · arxiv updated 2017/08/23 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In 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 an associated meta loss and optimization procedure to train them. To demonstrate our approach, we consider four diverse applications: human pose estimation, future prediction, image classification and segmentation. 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.