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Title: Dynamic Neural Relational Inference for Forecasting Trajectories
Understanding interactions between entities, e.g., joints of the human body, team sports players, etc., is crucial for tasks like forecasting. However, interactions between entities are commonly not observed and often hard to quantify. To address this challenge, recently, ‘Neural Relational Inference’ was introduced. It predicts static relations between entities in a system and provides an interpretable representation of the underlying system dynamics that are used for better trajectory forecasting. However, generally, relations between entities change as time progresses. Hence, static relations improperly model the data. In response to this, we develop Dynamic Neural Relational Inference (dNRI), which incorporates insights from sequential latent variable models to predict separate relation graphs for every time-step. We demonstrate on several real-world datasets that modeling dynamic relations improves forecasting of complex trajectories.  more » « less
Award ID(s):
1725729
NSF-PAR ID:
10190064
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Page Range / eLocation ID:
4383 to 4392
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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