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Found 3 papers, 2 papers with code
Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift
Natural distribution shift causes a deterioration in the perception performance of convolutional neural networks (CNNs).
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
Therefore, a process that mitigates false detections is crucial for both safety and accuracy.
Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration
We overcome these limitations by: (1) implementing loss attenuation in EfficientDet, and proposing two deterministic methods for the exact and fast propagation of the output distribution, (2) demonstrating on the KITTI and BDD100K datasets that the predicted uncertainty is miscalibrated, and adapting two calibration methods to the localization task, and (3) investigating the correlation between aleatoric uncertainty and task-relevant error sources.
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