Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection

Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

This paper examines the impact of different box merging strategies for sampling-based uncertainty estimation methods in object detection. Also, a comparison between the almost exclusively used softmax confidence scores and the predicted variances on the quality of the final predictions estimates is presented. The results suggest that estimated variances are a stronger predictor for the detection quality. However, variance-based merging strategies do not improve significantly over the confidence-based alternative for the given setup. In contrast, we show that different methods to estimate the uncertainty of the predictions have a significant influence on the quality of the ensembling outcome. Since mAP does not reward uncertainty estimates, such improvements were only noticeable on the resulting PDQ scores.

OriginalspracheEnglisch
TitelComputer Vision – ECCV 2020 Workshops, Proceedings
Redakteure/-innenAdrien Bartoli, Andrea Fusiello
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten3-10
Seitenumfang8
ISBN (Print)9783030654139
DOIs
PublikationsstatusVeröffentlicht - 2020
VeranstaltungWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, Großbritannien/Vereinigtes Königreich
Dauer: 23 Aug. 202028 Aug. 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12540 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

KonferenzWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtGlasgow
Zeitraum23/08/2028/08/20

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