Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction

arXiv:2403.07263v1 Announce Type: cross
Abstract: Quantifying a model's predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object detection. In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes. One challenge in doing so is that bounding box predictions are conditioned on the object's class label. Thus, we develop a novel two-step conformal approach that propagates uncertainty in predicted class labels into the uncertainty intervals …

abstract applications arxiv autonomous autonomous driving box challenge coverage cs.cv cs.lg detection driving object prediction predictive safety safety-critical stat.ml type uncertainty via