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.

本研究中，我们提出了一种利用预发布的电影预告片作为弱监督来检测电影关键时刻的模型。我们引入了一种新颖的排名网络，利用电影和预告片之间的共同关注作为指导，生成高度相关的训练数据对，并提出了一种对特征表示进行增强的对比关注模块，使关键和非关键时刻之间的对比最大化。我们构建了第一个电影-预告片数据集，并证明了所提出的方法的性能优越性。

学习完整电影中的预告片片段