Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

Understand

There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks.

• While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged.
• A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure.
• This paper provides the first in-depth investigation of the effect of different association and merging strategies.