Understanding movies and their structural patterns is a crucial task to decode the craft of video editing. While previous works have developed tools for general analysis such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the cut type recognition task, which requires modeling of multi-modal information. To ignite research in the new task, we construct a large-scale dataset called MovieCuts, which contains more than 170K videoclips labeled among ten cut types. We benchmark a series of audio-visual approaches, including some that deal with the problem's multi-modal and multi-label nature. Our best model achieves 45.7% mAP, which suggests that the task is challenging and that attaining highly accurate cut type recognition is an open research problem.

翻译：了解电影及其结构模式是解码视频编辑手工艺的关键任务。 虽然先前的作品开发了一般分析工具, 如在镜头水平上检测字符或识别电影摄影特性, 但用于理解最基本的视频编辑“ Cut ” 的努力却较少。 本文引入了剪切型识别任务, 需要建模多模式信息。 为了点燃新任务的研究, 我们建造了一个大型数据集, 名为“ MoveeCuts ”, 包含170K 以上10个剪切型的视频剪切片。 我们以一系列视听方法为基准, 包括一些处理问题多模式和多标签性质的方法。 我们的最佳模型实现了45.7%的 mAP, 这表明这项任务具有挑战性, 并且实现高度准确的剪切型识别是一个公开的研究问题。