Multiscale Video Pretraining for Long-Term Activity Forecasting
This addresses the challenge of expensive human annotations and poor generalization in long-term activity forecasting for video analysis, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of poor generalization in long-term activity forecasting by proposing Multiscale Video Pretraining (MVP), a self-supervised approach that learns to predict future video clips over multiple timescales, resulting in over 20% accuracy gain in video summary forecasting compared to state-of-the-art methods.
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.