AdSum: Two-stream Audio-visual Summarization for Automated Video Advertisement Clipping
This addresses the labor-intensive task of creating multiple ad versions for advertisers, but it is incremental as it adapts existing video summarization techniques to a specific domain.
The paper tackles the problem of automating video advertisement clipping by framing it as a shot selection problem, and their two-stream audio-visual fusion model outperforms state-of-the-art methods on metrics like Average Precision and Area Under Curve.
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.