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.

本文提出了一个基于多假设预测模型和元损失与优化函数训练框架以应对不确定性的可行方法，并在人体姿态估计、未来预测、图像分类和分割等四种不同应用场景中检验了该方法的有效性，发现多假设预测模型在所有情况下均优于单一假设预测模型，并可以同时揭示有关预测变异性的有价值的洞见。