  Anticipating future activities in video is a task with many practical
applications. While earlier approaches are limited to just a few seconds in the
future, the prediction time horizon has just recently been extended to several
minutes in the future. However, as increasing the predicted time horizon, the
future becomes more uncertain and models that generate a single prediction fail
at capturing the different possible future activities. In this paper, we
address the uncertainty modelling for predicting long-term future activities.
Both an action model and a length model are trained to model the probability
distribution of the future activities. At test time, we sample from the
predicted distributions multiple samples that correspond to the different
possible sequences of future activities. Our model is evaluated on two
challenging datasets and shows a good performance in capturing the multi-modal
future activities without compromising the accuracy when predicting a single
sequence of future activities.
