解决短视频剪辑中镜头顺序排列难题，提升视频叙事效果。
Shot Sequence Ordering for Video Editing: Benchmarks, Metrics, and Cinematology-Inspired Computing Methods
- 引入肯德尔τ距离作为排序评估指标，设计新损失函数。
- 构建两个公开数据集，准确率显著优于基线模型。
- 融合电影元数据与镜头标签，适合影视创作与AI编辑研究者。
随着短视频平台兴起，视频制作需求激增，但高质量内容仍依赖专业剪辑技巧和视觉语言理解。为推动这一领域发展，本文提出镜头序列排序（SSO）任务，并针对缺乏公开基准数据的瓶颈，构建了两个新数据集：AVE-Order和ActivityNet-Order。采用肯德尔τ距离作为评估指标，提出肯德尔τ距离-交叉熵损失函数。同时引入电影学嵌入（Cinematology Embedding），将电影元数据和镜头标签作为先验知识融入模型，构建AVE-Meta数据集验证有效性。实验表明，所提方法在排序准确率上显著提升。所有数据集已开源：https://github.com/litchiar/ShotSeqBench。
原文摘要 · Abstract (English)
With the rising popularity of short video platforms, the demand for video production has increased substantially. However, high-quality video creation continues to rely heavily on professional editing skills and a nuanced understanding of visual language. To address this challenge, the Shot Sequence Ordering (SSO) task in AI-assisted video editing has emerged as a pivotal approach for enhancing video storytelling and the overall viewing experience. Nevertheless, the progress in this field has been impeded by a lack of publicly available benchmark datasets. In response, this paper introduces two novel benchmark datasets, AVE-Order and ActivityNet-Order. Additionally, we employ the Kendall Tau distance as an evaluation metric for the SSO task and propose the Kendall Tau Distance-Cross Entropy Loss. We further introduce the concept of Cinematology Embedding, which incorporates movie metadata and shot labels as prior knowledge into the SSO model, and constructs the AVE-Meta dataset to validate the method's effectiveness. Experimental results indicate that the proposed loss function and method substantially enhance SSO task accuracy. All datasets are publicly accessible at https://github.com/litchiar/ShotSeqBench.
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