Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …

In weakly supervised video anomaly detection (WVAD), where only video-level labels
indicating the presence or absence of abnormal events are available, the primary challenge …

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 …

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 …

Abstract 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 …

Abstract We introduce Situation Monitor a novel zero-shot Outof-Distribution (OOD) detection
approach for transformer based object detection models to enhance reliability in …

In this paper, we propose a novel method to address the challenge of learning deep neural
network models in the presence of open-set noisy labels, which include mislabeled samples …

The goal for classification is to correctly assign labels to unseen samples. However, most
methods misclassify samples with unseen labels and assign them to one of the known …

Abstract Open-Set Recognition (OSR) is a problem with mainly practical applications.
However, recent evaluations have largely focused on small-scale data and tuning …

Advances in multimodal pre-training have propelled object-level foundation models, such as
Grounding DINO and Florence-2, in tasks like visual grounding and object detection …