Detecting Moments and Highlights in Videos via Natural Language Queries using Cross-Attention Transformer

In the context of rapidly growing video content on digital platforms, the efficient and accurate extraction of meaningful segments from videos is of paramount importance. Traditional methods for video summarization and highlighting often rely on manual annotation or predefined algorithms, which can limit their adaptability to diverse content. The proposed approach leverages the power of both natural language processing and video analysis. The system begins by employing advanced language models to comprehend and interpret natural language queries provided by users. These queries may consist of descriptive sentences or keywords that capture the essence of the desired video content. The system then processes the video using sophisticated video analysis techniques, including computer vision and deep learning, to identify segments that align with the semantics of the user's query. Key contributions of the paper include the integration of state-of-the-art language models and video analysis techniques, enabling the system to understand the nuanced relationships between language and visual content. The proposed method addresses the challenge of bridging the gap between textual and visual data by employing a joint approach that effectively retrieves relevant video segments based on the user's query. Prediction results will demonstrate the effectiveness of the Query based Video Highlights system in accurately identifying and extracting moments and highlights from a wide range of video content. The approach showcases significant improvements over traditional methods by utilizing the contextual information provided by natural language queries. Additionally, the paper highlights the adaptability and scalability of the approach across various domains and types of video content.