ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929419520573440 |
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| author | Takenaka, Patrick Maucher, Johannes Huber, Marco F. |
| author_facet | Takenaka, Patrick Maucher, Johannes Huber, Marco F. |
| contents | 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 the basis of new challenging scenarios we show that state-of-the-art video predictors struggle in complex dynamical settings, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a symbolically addressable interface between data-driven aspects in the model and our dedicated procedural knowledge module, which we utilize in downstream control tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge Takenaka, Patrick Maucher, Johannes Huber, Marco F. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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 the basis of new challenging scenarios we show that state-of-the-art video predictors struggle in complex dynamical settings, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a symbolically addressable interface between data-driven aspects in the model and our dedicated procedural knowledge module, which we utilize in downstream control tasks. |
| title | ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.09537 |