Adiabatic quantum computing (AQC) is a promising quantum computing approach for discrete and often NP-hard optimization problems.
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems.
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality.
This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data.
Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228, 000 action sequences.
This work addresses the problem of semantic scene understanding under foggy road conditions.
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.
Comprehensive semantic segmentation is one of the key components for robust scene understanding and a requirement to enable autonomous driving.
Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology.
In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction.