  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. Additionally, we propose a
Contrastive Attention module to enhance the feature representations such that
the comparative contrast between features of the key and non-key moments are
maximized. We construct the first movie-trailer dataset, and the proposed
Co-Attention assisted ranking network shows superior performance even over the
supervised approach. The effectiveness of our Contrastive Attention module is
also demonstrated by the performance improvement over the state-of-the-art on
the public benchmarks.
