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 …

Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning
models in the wild. One of the key challenges is that models lack supervision signals from …

To promote the safe application of detectors, a task of unsupervised out-of-distribution object
detection (OOD-OD) is recently proposed, whose goal is to detect unseen OOD objects …

Feature embeddings derived from continuous mapping using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …

While deep neural network (DNN) models have become extremely popular for object
detection in automated driving systems (ADS), the dynamic and varied nature of the road …

We address the problem of out-of-distribution (OOD) detection for the task of object
detection. We show that residual convolutional layers with batch normalisation produce …

The recently proposed open-world object and open-set detection have achieved a
breakthrough in finding never-seen-before objects and distinguishing them from known …

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of
machine learning systems in the real world. Great progress has been made over the past …

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 …

Extracting in-distribution (ID) images from noisy images scraped from the Internet is an
important preprocessing for constructing datasets, which has traditionally been done …