Multiscale Video Pretraining for Long-Term Activity Forecasting
Reuben Tan Matthias De Lange Michael Iuzzolino Bryan A. Plummer Kate Saenko Karl Ridgeway Lorenzo Torresani
Long-term activity forecasting is an especially challenging research problem
because it requires understanding the temporal relationships between observed
actions, as well as the variability and complexity of human activities. Despite
relying on strong supervision via expensive human annotations, state-of-the-art
forecasting approaches often generalize poorly to unseen data. To alleviate
this issue, we propose Multiscale Video Pretraining (MVP), a novel
self-supervised pretraining approach that learns robust representations for
forecasting by learning to predict contextualized representations of future
video clips over multiple timescales. MVP is based on our observation that
actions in videos have a multiscale nature, where atomic actions typically
occur at a short timescale and more complex actions may span longer timescales.
We compare MVP to state-of-the-art self-supervised video learning approaches on
downstream long-term forecasting tasks including long-term action anticipation
and video summary prediction. Our comprehensive experiments across the Ego4D
and Epic-Kitchens-55/100 datasets demonstrate that MVP out-performs
state-of-the-art methods by significant margins. Notably, MVP obtains a
relative performance gain of over 20% accuracy in video summary forecasting
over existing methods.