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2021 | OriginalPaper | Buchkapitel

Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks

verfasst von : Florian Heidecker, Abdul Hannan, Maarten Bieshaar, Bernhard Sick

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

State-of-the-art instance segmentation techniques currently provide a bounding box, class, mask, and scores for each instance. What they do not provide is an epistemic uncertainty estimate of these predictions. With our approach, we want to identify corner cases by considering the epistemic uncertainty. Corner cases are data/situations that are underrepresented or not covered in our data set. Our work is based on Mask R-CNN. We estimate the epistemic uncertainty by extending the architecture with Monte-Carlo dropout layers. By repeatedly executing the forward pass, we create a large number of predictions per instance. Afterward, we cluster the predictions of an instance based on the bounding box coordinates. It becomes possible to determine the epistemic position uncertainty for the bounding boxes and the classifier’s epistemic class uncertainty. For the epistemic uncertainty regarding the bounding box position and the class assignment, we provide a criterion for detecting corner cases utilizing the model’s epistemic uncertainty.

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Metadaten
Titel
Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks
verfasst von
Florian Heidecker
Abdul Hannan
Maarten Bieshaar
Bernhard Sick
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-68799-1_26