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.
CITATION STYLE
Heidecker, F., Hannan, A., Bieshaar, M., & Sick, B. (2021). Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12664 LNCS, pp. 361–374). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68799-1_26
Mendeley helps you to discover research relevant for your work.