Out-of-distribution (OOD) detection has received much attention lately due to its importance
in the safe deployment of neural networks. One of the key challenges is that models lack …

Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical
yet underexplored. One of the key challenges is that models lack supervision signals from …

Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object
detectors in the wild. Although distance-based OOD detection methods have demonstrated …

Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …

Real-world deployment of reliable object detectors is crucial for applications such as
autonomous driving. However, general-purpose object detectors like Faster R-CNN are …

Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-
set OOD data. Inspired by recent advancements in text-to-image generative models, such as …

Driver fatigue is one of the leading causes of accidents worldwide. One of the most reliable
methods of measuring driver fatigue is to detect the driver's drowsiness. Drowsiness and …

We present an application of conformal prediction, a form of uncertainty quantification with
guarantees, to the detection of railway signals. State-of-the-art architectures are tested and …

Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based
perception stacks for safetycritical applications such as autonomous driving or Unmanned …

Deploying deep learning models in real-world certi ed systems requires the ability to provide
con dence estimates that accurately reflect their uncertainty. In this paper, we demonstrate …