arXiv:2209.12164v2 Announce Type: replace-cross
Abstract: 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
Expert Commentary: M-SAN for Advertisement Video Editing
Advertising video editing is a crucial aspect of multimedia information systems, where the goal is to efficiently distill important information from longer advertisements into shorter videos. This process involves complex tasks such as video segmentation and segment assemblage, which require a multi-disciplinary approach drawing from fields like Artificial Reality, Augmented Reality, and Virtual Realities.
The proposed M-SAN (Multi-modal Segment Assemblage Network) in this research is a significant innovation that tackles the challenges faced in the segment assemblage stage of video editing. By leveraging multi-modal representations and incorporating an Encoder-Decoder Ptr-Net framework with an Attention mechanism, M-SAN shows promise in achieving coherent and efficient segment assemblage end-to-end.
One key aspect of the M-SAN approach is the design of an importance-coherence reward for training the network. This reward mechanism plays a critical role in ensuring that the edited videos not only retain crucial content but also maintain coherence in the narrative flow. This emphasis on importance and coherence aligns well with the objectives of modern multimedia content creation.
The experimental evaluation of M-SAN on the Ads-1k dataset demonstrates its superiority over random selection and previous methods in terms of the proposed metric Imp-Coh@Time. This unified metric, which evaluates importance, coherence, and duration of the edited videos simultaneously, provides a comprehensive understanding of the performance of the method.
Overall, the M-SAN approach represents a significant advancement in the field of advertisement video editing within multimedia information systems. Its utilization of multi-modal representations and emphasis on importance-coherence reward showcase the potential for future developments in automated video editing technologies.