AdSum：用于自动化视频广告剪辑的双流音频视觉摘要
AdSum: Two-stream Audio-visual Summarization for Automated Video Advertisement Clipping
AI总结：
AdSum提出了一种双流音频视觉融合模型，用于自动化视频广告剪辑，通过重要性预测提升广告剪辑效率。
摘要：
广告商通常需要为同一个广告制作不同时长的多个版本。传统方法是手动选择并重新剪辑较长的视频广告中的镜头以创建较短版本，这费时费力。在本文中，我们介绍了一种利用视频摘要技术进行自动化视频广告剪辑的框架。我们首次将视频剪辑框架定为镜头选择问题，专门针对广告领域。与现有通用视频摘要方法主要关注视觉内容不同，我们的方法强调音频在广告中的关键作用。为此，我们开发了一个双流音频视觉融合模型，预测视频帧的重要性，其中重要性定义为在最终制作的短广告中被选择的可能性。为了解决缺乏广告专用数据集的问题，我们提出了AdSum204，一个包含102对30秒和15秒广告的真实广告活动数据集。广泛的实验表明，我们的模型在多个指标上均优于最先进的方法，包括平均精度、曲线下面积、斯皮尔曼和肯德尔。数据集和代码可在https://github.com/ostadabbas/AdSum204上获得。
英文摘要：
Advertisers commonly need multiple versions of the same advertisement (ad) at varying durations for a single campaign. The traditional approach involves manually selecting and re-editing shots from longer video ads to create shorter versions, which is labor-intensive and time-consuming. In this paper, we introduce a framework for automated video ad clipping using video summarization techniques. We are the first to frame video clipping as a shot selection problem, tailored specifically for advertising. Unlike existing general video summarization methods that primarily focus on visual content, our approach emphasizes the critical role of audio in advertising. To achieve this, we develop a two-stream audio-visual fusion model that predicts the importance of video frames, where importance is defined as the likelihood of a frame being selected in the firm-produced short ad. To address the lack of ad-specific datasets, we present AdSum204, a novel dataset comprising 102 pairs of 30-second and 15-second ads from real advertising campaigns. Extensive experiments demonstrate that our model outperforms state-of-the-art methods across various metrics, including Average Precision, Area Under Curve, Spearman, and Kendall. The dataset and code are available at https://github.com/ostadabbas/AdSum204.
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。
