5 citations · 12 across the 8 of their papers we have counts for
8 papers
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…
Divide and Conquer: A Systematic Approach for Industrial Scale High-Definition OpenDRIVE Generation from Sparse Point Clouds
Leon Eisemann, Johannes Maucher
High-definition road maps play a crucial role in the functionality and verification of highly automated driving functions. These contain precise information about the road network,…
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…