14 citations · 49 across the 16 of their papers we have counts for
16 papers
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation
Clive Tinashe Marimo, Benedikt Blumenstiel, Maximilian Nitsche +2
Vision-language models for Earth observation (EO) typically rely on the visual spectrum of data as the only model input, thus failing to leverage the rich spectral information avai…
Explainability in AI Based Applications: A Framework for Comparing Different Techniques
Arne Grobrugge, Nidhi Mishra, Johannes Jakubik +1
The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, an…
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
Simeon Allmendinger, Patrick Hemmer, Moritz Queisner +5
Recent advances in synthetic imaging open up opportunities for obtaining additional data in the field of surgical imaging. This data can provide reliable supplements supporting sur…
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Leopold Müller, Patrick Hemmer, Moritz Queisner +5
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths. While this tas…
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Johannes Jakubik, Daniel Weber, Patrick Hemmer +2
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and c…
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
Benedikt Blumenstiel, Johannes Jakubik, Hilde Kühne +1
While semantic segmentation has seen tremendous improvements in the past, there are still significant labeling efforts necessary and the problem of limited generalization to classe…
Learning to Defer with Limited Expert Predictions
Patrick Hemmer, Lukas Thede, Michael Vössing +2
Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learnin…
AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions
Jakob Schoeffer, Johannes Jakubik, Michael Voessing +2
In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In…
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation
Johannes Jakubik, Michal Muszynski, Michael Vössing +2
Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natur…
Data-Centric Artificial Intelligence
Johannes Jakubik, Michael Vössing, Niklas Kühl +2
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effe…
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
Johannes Jakubik, Jakob Schöffer, Vincent Hoge +2
In this work, we empirically examine human-AI decision-making in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker…
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Johannes Jakubik, Benedikt Blumenstiel, Michael Vössing +1
Business analytics and machine learning have become essential success factors for various industries - with the downside of cost-intensive gathering and labeling of data. Few-shot…
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing +2
Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to d…
Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler
Johannes Jakubik, Michael Vössing, Dominik Bär +2
The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated throug…
Directed particle swarm optimization with Gaussian-process-based function forecasting
Johannes Jakubik, Adrian Binding, Stefan Feuerriegel
Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with…