Takenaka, PatrickPatrickTakenakaMaucher, JohannesJohannesMaucherHuber, Marco F.Marco F.Huber2024-07-232024-07-232023https://publica.fraunhofer.de/handle/publica/47200210.1109/ICCVW60793.2023.001162-s2.0-85182921194We 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 deep models and show that this leads to a better performance than using data-driven models alone. We develop an architecture that facilitates latent space disentanglement in order to use the integrated procedural knowledge, and establish a setup that allows the model to learn the procedural interface in the latent space using the downstream task of video prediction. We contrast the performance to a state-of-the-art data-driven approach and show that problems where purely data-driven approaches struggle can be handled by using knowledge about the domain, providing an alternative to simply collecting more data.enInformed Machine LearningNeuro Symbolic AIVideo Predictionconference paper