Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Abstract Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in
computer vision. Its image generation applications, such as Imagen, Latent Diffusion …

Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …

Diffusion models have shown impressive performance for generative modelling of images.
In this paper, we present a novel semantic segmentation method based on diffusion models …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Driving requires interacting with road agents and predicting their future behaviour in order to
navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view …

Spotting objects that are visually adapted to their surroundings is challenging for both
humans and AI. Conventional generic/salient object detection techniques are suboptimal for …

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

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 …