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Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes.
A powerful rank-based correction to multiple testing under positive dependency
We develop a novel multiple hypothesis testing correction with family-wise error rate (FWER) control that efficiently exploits positive dependencies between potentially correlated statistical hypothesis tests.
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation
Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world.
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
Adversarial patch attacks are an emerging security threat for real world deep learning applications.
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