4 citations · 4 across the 11 of their papers we have counts for
11 papers
Differences in Detection: Explainability Where it Matters
Johannes Theodoridis, Johannes Maucher, Andreas Schilling
We propose Differences in Detection (DnD), an intuitive method to compare two object detection models. Based on the same matching algorithm, it complements the standard metrics of…
Evaluating the Impact of Data Anonymization on Image Retrieval
Marvin Chen, Manuel Eberhardinger, Johannes Maucher
With the growing importance of privacy regulations such as the General Data Protection Regulation, anonymizing visual data is becoming increasingly relevant across institutions. Ho…
Generation of Programmatic Rules for Document Forgery Detection Using Large Language Models
Valentin Schmidberger, Manuel Eberhardinger, Setareh Maghsudi +1
Document forgery poses a growing threat to legal, economic, and governmental processes, requiring increasingly sophisticated verification mechanisms. One approach involves the use…
A Toolbox for Improving Evolutionary Prompt Search
Daniel Grießhaber, Maximilian Kimmich, Johannes Maucher +1
Evolutionary prompt optimization has demonstrated effectiveness in refining prompts for LLMs. However, existing approaches lack robust operators and efficient evaluation mechanisms…
ViPro-2: Unsupervised State Estimation via Integrated Dynamics for Guiding Video Prediction
Patrick Takenaka, Johannes Maucher, Marco F. Huber
Predicting future video frames is a challenging task with many downstream applications. Previous work has shown that procedural knowledge enables deep models for complex dynamical…
Anonymization of Documents for Law Enforcement with Machine Learning
Manuel Eberhardinger, Patrick Takenaka, Daniel Grießhaber +1
The steadily increasing utilization of data-driven methods and approaches in areas that handle sensitive personal information such as in law enforcement mandates an ever increasing…
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming
Manuel Eberhardinger, Florian Rupp, Johannes Maucher +1
Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions. Incomprehensibility, however, is not an option for the use of (deep)…
Classification of Inkjet Printers based on Droplet Statistics
Patrick Takenaka, Manuel Eberhardinger, Daniel Grießhaber +1
Knowing the printer model used to print a given document may provide a crucial lead towards identifying counterfeits or conversely verifying the validity of a real document. Inkjet…
ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge
Patrick Takenaka, Johannes Maucher, Marco F. Huber
We propose a novel architecture design for video prediction in order to utilize procedural domain knowledge directly as part of the computational graph of data-driven models. On th…
Guiding Video Prediction with Explicit Procedural Knowledge
Patrick Takenaka, Johannes Maucher, Marco F. Huber
We propose a general way to integrate procedural knowledge of a domain into deep learning models. We apply it to the case of video prediction, building on top of object-centric dee…
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions co…