Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

We present a novel vision-language prompt learning approach for few-shot out-of-
distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from …

Open-set object recognition aims to identify if an object is from a class that has been
encountered during training or not. To perform open-set object recognition accurately, a key …

Machine learning models deployed in the wild can be challenged by out-of-distribution
(OOD) data from unknown classes. Recent advances in OOD detection rely on distance …

This survey reviews advances in 3D object detection approaches for autonomous driving. A
brief introduction to 2D object detection is first discussed and drawbacks of the existing …

Reliable detection of various objects and road users in the surrounding environment is
crucial for the safe operation of automated driving systems (ADS). Despite recent progresses …

Abstract Before developing a Document Layout Analysis (DLA) model in real-world
applications conducting comprehensive robustness testing is essential. However the …

Using unlabeled data to regularize the machine learning models has demonstrated promise
for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the …

In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative
that DNNs provide inference robust to external perturbations-both intentional and …