论文标题
学习预告片中的全长电影
Learning Trailer Moments in Full-Length Movies
论文作者
论文摘要
电影的关键时刻在剧本中脱颖而出，吸引了观众的注意力，并使电影浏览效率高。但是缺乏注释会使现有方法不适用于电影关键力矩检测。为了摆脱人类注释，我们利用官方发行的预告片作为薄弱的监督，学习一个可以从全长电影中检测到关键时刻的模型。我们介绍了一个新颖的排名网络，该网络利用电影和预告片之间的共同发作作为产生训练对的指导，在这种指导下，预计将在预告片中高度校正的时刻预计得分高于不相关的时刻。此外，我们提出了一个对比度注意模块，以增强特征表示形式，以使密钥和非钥匙矩的特征之间的比较对比度最大化。我们构建了第一个电影拖车数据集，而拟议的共同发起的辅助排名网络甚至在监督方法上都显示出卓越的性能。我们的对比性关注模块的有效性也通过对公共基准的最新表现的性能提高也证明了。
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.
