76 citations · 205 across the 15 of their papers we have counts for
17 papers
Online Out-of-Domain Detection for Automated Driving
Timo Sämann, Horst-Michael Groß
Ensuring safety in automated driving is a major challenge for the automotive industry. Special attention is paid to artificial intelligence, in particular to Deep Neural Networks (…
PanopticNDT: Efficient and Robust Panoptic Mapping
Daniel Seichter, Benedict Stephan, Söhnke Benedikt Fischedick +3
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate…
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
Dustin Aganian, Mona Köhler, Benedict Stephan +2
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize hu…
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
Dustin Aganian, Mona Köhler, Sebastian Baake +2
As the use of collaborative robots (cobots) in industrial manufacturing continues to grow, human action recognition for effective human-robot collaboration becomes increasingly imp…
Efficient Multi-Task Scene Analysis with RGB-D Transformers
Söhnke Benedikt Fischedick, Daniel Seichter, Robin Schmidt +2
Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the s…
ATTACH Dataset: Annotated Two-Handed Assembly Actions for Human Action Understanding
Dustin Aganian, Benedict Stephan, Markus Eisenbach +2
With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus. For a cobot to act autonomously and as an assistant…
A Little Bit Attention Is All You Need for Person Re-Identification
Markus Eisenbach, Jannik Lübberstedt, Dustin Aganian +1
Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some t…
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning
Thomas Schnürer, Malte Probst, Horst-Michael Gross
Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to interpret the knowledge and reuse it across tasks. Inductive biases can address su…
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Daniel Seichter, Söhnke Benedikt Fischedick, Mona Köhler +1
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about indi…
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Timo Sämann, Ahmed Mostafa Hammam, Andrei Bursuc +2
Averaging predictions of a deep ensemble of networks is apopular and effective method to improve predictive performance andcalibration in various benchmarks and Kaggle competitions…
Efficient and Robust Semantic Mapping for Indoor Environments
Daniel Seichter, Patrick Langer, Tim Wengefeld +3
A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objec…
Few-Shot Object Detection: A Comprehensive Survey
Mona Köhler, Markus Eisenbach, Horst-Michael Gross
Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To a…
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
Daniel Seichter, Mona Köhler, Benjamin Lewandowski +2
Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assiste…
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization
Alexander Katzmann, Oliver Taubmann, Stephen Ahmad +3
Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major…
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Kai Fischer, Martin Simon, Florian Oelsner +3
Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initi…
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Martin Simon, Karl Amende, Andrea Kraus +5
Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robot…
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Martin Simon, Stefan Milz, Karl Amende +1
Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and moti…