论文标题
匹配切割：通过光滑的视觉过渡找到切割
Match Cutting: Finding Cuts with Smooth Visual Transitions
论文作者
论文摘要
匹配切割是一对使用类似的框架，组成或动作的镜头之间的过渡，以使观众从一个场景中流动。电影，电视和广告中经常使用比赛。但是，找到合作的镜头是一个高度手动且耗时的过程，可能需要几天的时间。我们提出了一个模块化和灵活的系统，以有效地找到从数百万射门开始的高质量匹配裁切候选人。我们使用分类和度量学习方法来注释并发布一个大约20k标记的对评估系统的数据集，以利用各种图像，视频，音频和视听功能提取器。此外，我们发布代码和嵌入式，用于在github.com/netflix/matchcut上复制实验。
A match cut is a transition between a pair of shots that uses similar framing, composition, or action to fluidly bring the viewer from one scene to the next. Match cuts are frequently used in film, television, and advertising. However, finding shots that work together is a highly manual and time-consuming process that can take days. We propose a modular and flexible system to efficiently find high-quality match cut candidates starting from millions of shot pairs. We annotate and release a dataset of approximately 20k labeled pairs that we use to evaluate our system, using both classification and metric learning approaches that leverage a variety of image, video, audio, and audio-visual feature extractors. In addition, we release code and embeddings for reproducing our experiments at github.com/netflix/matchcut.
