Enhancing Ad Break Detection in ADVisor: A Multimodal Approach to Minimizing Errors and Optimizing Relevance

In recent years, advancements in Artificial Intelligence (AI) have transformed numerous industries, including digital advertising. AI based systems now leverage image recognition, natural language processing, and speech recognition to optimize ad placement by analyzing large datasets in real time, providing more personalized and engaging experiences for viewers. However, the detection of suitable ad break points remains a challenge, often requiring manual intervention to avoid false positives and irrelevant placements. In this work, we introduce a multimodal method that combines information from video streams, audio, and transcripts to address these challenges in ADVisor, an AWS based platform designed for automatic ad placement. The goal is to rearrange the proposed points, particularly reducing false positives, while optimizing the relevance of the identified ad break points. By improving the precision of ad break detection, this method reduces the need for manual oversight and enhances the efficiency of the advertising workflow. Through extensive testing, the proposed approach demonstrates a significant reduction in false positives and an overall improvement in the accuracy of ad break detection. This work not only highlights the potential for multimodal processing to set new standards in computational advertising but also establishes a foundation for future enhancements in automating media content monetization.