Instance-Level Post Hoc Uncertainty Quantification in Object Detection 事件

Instance-Level Post Hoc Uncertainty Quantification in Object Detection arXiv:2606.04656v1 Announce Type: new Abstract: Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncertainty quantification without retraining aligns with real-world deployment requirements; therefore, we employ the Laplace approximation. Because instance-level uncertainty is needed, linearized inferen