| Title: |
Semi-Supervised Generative Models for Multi-Agent Trajectories |
| Authors: |
Brefeld, Ulf; Fassmeyer, Dennis; Fassmeyer, Pascal |
| Contributors: |
Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. |
| Source: |
Brefeld, U, Fassmeyer, D & Fassmeyer, P 2022, Semi-Supervised Generative Models for Multi-Agent Trajectories. in S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho & A Oh (eds), Advances in Neural Information Processing Systems 35 : 36th Conference on Neural Information Processing Systems (NeurIPS 2022). vol. 48, Advances in Neural Information Processing Systems, vol. 35, Curran Associates, Red Hook, pp. 37267-37281, 36th Conference on Neural Information Processing Systems - NeurIPS 2022, New Orleans, Louisiana, United States, 28.11.22. < https://papers.nips.cc/paper_files/paper/2022 > |
| Publisher Information: |
Curran Associates |
| Publication Year: |
2022 |
| Collection: |
Leuphana University of Lüneburg: Forschungsindex FOX |
| Subject Terms: |
/dk/atira/pure/core/keywords/informatics; name=Informatics; /dk/atira/pure/core/keywords/547106742; name=Business informatics |
| Description: |
Analyzing the spatiotemporal behavior of multiple agents is of great interest to many communities. Existing probabilistic models in this realm are formalized either in an unsupervised framework, where the latent space is described by discrete or continuous variables, or in a supervised framework, where weakly preserved labels add explicit information to continuous latent representations. To overcome inherent limitations, we propose a novel objective function for processing multi-agent trajectories based on semi-supervised variational autoencoders, where equivariance and interaction of agents are captured via customized graph networks. The resulting architecture disentangles discrete and continuous latent effects and provides a natural solution for injecting expensive domain knowledge into interactive sequential systems. Empirically, our model not only outperforms various state-of-the-art baselines in trajectory forecasting, but also learns to effectively leverage unsupervised multi-agent sequences for classification tasks on interactive real-world sports datasets. |
| Document Type: |
other/unknown material |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/isbn/978-1-7138-7108-8 |
| Availability: |
http://fis.leuphana.de/de/publications/semisupervised-generative-models-for-multiagent-trajectories(0dca34d8-743f-4946-9850-9c571409f5ab).html; https://www.scopus.com/pages/publications/85163202847; https://www.proceedings.com/content/068/068431webtoc.pdf; https://papers.nips.cc/paper_files/paper/2022; https://papers.nips.cc/paper_files/paper/2022/hash/f1fb6b2746332167f6670655372186cb-Abstract-Conference.html |
| Rights: |
info:eu-repo/semantics/openAccess ; http://rightsstatements.org/vocab/InC/1.0/ |
| Accession Number: |
edsbas.FA6BE04A |
| Database: |
BASE |