161 citations · 338 across the 33 of their papers we have counts for
5 papers · 1 filter
DenseBEV: Transforming BEV Grid Cells into 3D Objects
Marius Dähling, Sebastian Krebs, J. Marius Zöllner
In current research, Bird's-Eye-View (BEV)-based transformers are increasingly utilized for multi-camera 3D object detection. Traditional models often employ random queries as anch…
Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey
Albert Schotschneider, Svetlana Pavlitska, J. Marius Zöllner
Deep neural networks (DNNs) are widely used in perception systems for safety-critical applications, such as autonomous driving and robotics. However, DNNs remain vulnerable to vari…
DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation
Annemarie Hoffsommer, Helen Schneider, Svetlana Pavlitska +1
Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing. Emotion p…
CrowdQuery: Density-Guided Query Module for Enhanced 2D and 3D Detection in Crowded Scenes
Marius Dähling, Sebastian Krebs, J. Marius Zöllner
This paper introduces a novel method for end-to-end crowd detection that leverages object density information to enhance existing transformer-based detectors. We present CrowdQuery…
Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph
Ferdinand Mütsch, Maximilian Zipfl, Nikolai Polley +1
Scenario-based testing is an indispensable instrument for the comprehensive validation and verification of automated vehicles (AVs). However, finding a manageable and finite, yet r…