Learning Trailer Moments in Full-Length Movies

Understand

A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient.

• But a lack of annotations makes the existing approaches not applicable to movie key moment detection.
• To get rid of human annotations, we leverage the officially-released trailers as the weak supervision to learn a model that can detect the key moments from full-length movies.
• We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments.