论文标题
多模式段的组合网络，用于广告视频编辑，并具有重要的互补奖励
Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward
论文作者
论文摘要
广告视频编辑旨在将广告视频自动编辑为较短的视频，同时保留广告商传达的连贯内容和关键信息。它主要包含两个阶段：视频分割和段组合。现有方法在视频分割阶段表现良好，但遭受了对额外繁琐模型的依赖性问题，并且在细分组合阶段的性能差。为了解决这些问题，我们提出了M-SAN（多模式段组合网络），该网络可以执行高效且连贯的段组合任务端到端。它利用从段中提取的多模式表示，并遵循带有注意机制的编码器ptr-decoder ptr-net框架。重要性互补奖励是为培训M-SAN设计的。在广告客户收集的丰富广告方案下，我们在ADS-1K数据集上进行了1000多个视频的实验。为了评估这些方法，我们提出了一个统一的imp-coh@Time，该指标可以全面评估同时评估产出的重要性，相干性和持续时间。实验结果表明，我们的方法比随机选择和公制上的先前方法更好的性能。消融实验进一步验证了多模式表示和重要性互动的奖励可显着提高性能。 ADS-1K数据集可用：https：//github.com/yunlong10/ads-1k
Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers. It mainly contains two stages: video segmentation and segment assemblage. The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage. To address these problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can perform efficient and coherent segment assemblage task end-to-end. It utilizes multi-modal representation extracted from the segments and follows the Encoder-Decoder Ptr-Net framework with the Attention mechanism. Importance-coherence reward is designed for training M-SAN. We experiment on the Ads-1k dataset with 1000+ videos under rich ad scenarios collected from advertisers. To evaluate the methods, we propose a unified metric, Imp-Coh@Time, which comprehensively assesses the importance, coherence, and duration of the outputs at the same time. Experimental results show that our method achieves better performance than random selection and the previous method on the metric. Ablation experiments further verify that multi-modal representation and importance-coherence reward significantly improve the performance. Ads-1k dataset is available at: https://github.com/yunlong10/Ads-1k
