English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Long-term activity forecasting is an especially challenging research problembecause it requires understanding the temporal relationships between observedactions, as well as the variability and complexity of human activities. Despiterelying on strong supervision via expensive human annotations, state-of-the-artforecasting approaches often generalize poorly to unseen data. To alleviatethis issue, we propose Multiscale Video Pretraining (MVP), a novelself-supervised pretraining approach that learns robust representations forforecasting by learning to predict contextualized representations of futurevideo clips over multiple timescales. MVP is based on our observation thatactions in videos have a multiscale nature, where atomic actions typicallyoccur 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 ondownstream long-term forecasting tasks including long-term action anticipationand video summary prediction. Our comprehensive experiments across the Ego4Dand Epic-Kitchens-55/100 datasets demonstrate that MVP out-performsstate-of-the-art methods by significant margins. Notably, MVP obtains arelative performance gain of over 20% accuracy in video summary forecastingover existing methods.

Multiscale Video Pretraining for Long-Term Activity Forecasting