Benchmarking Sampling-based Probabilistic Object Detectors.Open Website

2019 (modified: 10 Nov 2022)CVPR Workshops2019Readers: Everyone
Abstract: This paper provides the first benchmark for sampling- based probabilistic object detectors. A probabilistic object detector expresses uncertainty for all detections that reliably indicates object localisation and classification performance. We compare performance for two sampling-based uncertainty techniques, namely Monte Carlo Dropout and Deep Ensembles, when implemented into one-stage and two-stage object detectors, Single Shot MultiBox Detector and Faster R-CNN. Our results show that Deep Ensembles outperform MC Dropout for both types of detectors. We also introduce a new merging strategy for sampling-based techniques and one-stage object detectors. We show this novel merging strategy has competitive performance with previously established strategies, while only having one free parameter.
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