4 citations · 4 across the 5 of their papers we have counts for
5 papers
Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
Jingyi Yu, Tim Pychynski, Marco F. Huber
To gain deeper insights into a complex sensor system through the lens of causality, we present common and individual causal mechanism estimation (CICME), a novel three-step approac…
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…
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…
Improving the Effectiveness of Deep Generative Data
Ruyu Wang, Sabrina Schmedding, Marco F. Huber
Recent deep generative models (DGMs) such as generative adversarial networks (GANs) and diffusion probabilistic models (DPMs) have shown their impressive ability in generating high…