Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier
of p (class| pixel feature). Though straightforward, this de facto paradigm neglects the …

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …

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 …

Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world …

In the past decade, deep learning has been widely applied across various fields. However,
its applicability in open-world scenarios is often limited due to the lack of quantifying …

Anomaly segmentation is a critical task for driving applications, and it is approached
traditionally as a per-pixel classification problem. However, reasoning individually about …

State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are
usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle …

Anomaly detection can be conceived either through generative modelling of regular training
data or by discriminating with respect to negative training data. These two approaches …

Abstract State-of-the-art (SOTA) anomaly segmentation approaches on complex urban
driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or …

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 …