镜头序列排序用于视频编辑：基准、指标与电影学启发的计算方法
Shot Sequence Ordering for Video Editing: Benchmarks, Metrics, and Cinematology-Inspired Computing Methods
AI总结：
针对视频编辑中镜头序列排序任务缺乏基准的问题，本文提出AVE-Order和ActivityNet-Order数据集、Kendall Tau距离损失及电影学嵌入方法，显著提升排序准确性。
摘要：
随着短视频平台的日益普及，视频制作的需求大幅增加。然而，高质量视频创作仍然严重依赖专业编辑技能和对视觉语言的细致理解。为应对这一挑战，AI辅助视频编辑中的镜头序列排序（SSO）任务已成为增强视频叙事和整体观看体验的关键方法。尽管如此，该领域的进展因缺乏公开可用的基准数据集而受阻。为此，本文引入了两个新颖的基准数据集：AVE-Order和ActivityNet-Order。此外，我们采用Kendall Tau距离作为SSO任务的评估指标，并提出了Kendall Tau距离-交叉熵损失。我们进一步引入了电影学嵌入的概念，将电影元数据和镜头标签作为先验知识纳入SSO模型，并构建了AVE-Meta数据集以验证该方法的有效性。实验结果表明，所提出的损失函数和方法显著提升了SSO任务的准确性。所有数据集均可在https://github.com/litchiar/ShotSeqBench公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
