Distilling Knowledge for Short-to-Long Term Trajectory Prediction

arXiv:2305.08553v3 Announce Type: replace-cross
Abstract: Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics. One fundamental difficulty stands in the evolution of the trajectory that becomes more and more uncertain and unpredictable as the time horizon grows, subsequently increasing the complexity of the problem. To overcome this issue, in this paper, we propose Di-Long, a new method that employs the distillation of a short-term trajectory model forecaster that guides a …

abstract arxiv complexity computer computer vision cs.ai cs.cv cs.lg cs.ro evolution fields forecasting horizon knowledge long-term machine machine learning prediction robotics trajectory type uncertain vision