Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
Alexander Timans Christoph-Nikolas Straehle Kaspar Sakmann Eric Nalisnick
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 for the bounding boxes. This broadens the validity of
our conformal coverage guarantees to include incorrectly classified objects,
ensuring their usefulness when maximal safety assurances are required.
Moreover, we investigate novel ensemble and quantile regression formulations to
ensure the bounding box intervals are adaptive to object size, leading to a
more balanced coverage across sizes. Validating our two-step approach on
real-world datasets for 2D bounding box localization, we find that desired
coverage levels are satisfied with actionably tight predictive uncertainty
intervals.