Search Results for author:
Found 7 papers, 3 papers with code
Uncertainty Calibration and its Application to Object Detection
We integrate additional position information in our examinations to evaluate the effect of the object's position on the semantic calibration properties.
Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection
Our experiments show that common detection models overestimate the spatial uncertainty in comparison to the observed error.
Confidence Calibration for Object Detection and Segmentation
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis.
Bayesian Confidence Calibration for Epistemic Uncertainty Modelling
We introduce Bayesian confidence calibration - a framework to obtain calibrated confidence estimates in conjunction with an uncertainty of the calibration method.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale.
Multivariate Confidence Calibration for Object Detection
Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods.
