Before revision:  Our main contributions in this work are the creation of a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps, and a message passing graph neural network ( GNN ) that operates on these graphs to produce the desired likelihood for every association therein.
After revision:  Our main contributions in this work are the creation of a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps, and a message passing graph neural network ( MPNN ) that operates on these graphs to produce the desired likelihood for every association therein.