Task: Summarization
Input (English): We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.
Output (English): This paper presents a graph neural network based architecture that is trained to locate and model the interactions of agents in an environment directly from pixels and show advantage of model for tracking tasks and forecasting agent locations.