Leveraging Natural Language Queries for Effective Video Analysis

With the exponential growth of video content, the demand for automatic video analysis using natural language queries has surged. However, the integration of these two tasks has only recently gained attention, and the challenge of addressing dynamic background changes in videos remains largely unexplored. Additionally, efficiently retrieving relevant videos from vast collections poses a significant hurdle, despite some proposed solutions in the literature. Consequently, this research area remains understudied and emerging. To address these challenges, this paper introduces a novel framework designed to efficiently cater to user demands and solve problems related to video analysis. The proposed framework leverages an encoder and decoder model, employing a combination of multiple learning approaches to retrieve key moments and detect highlights within visual content. To validate the effectiveness of the proposed model, a comprehensive comparative analysis is presented, utilizing various performance measures on publicly available datasets. The experimental results conclusively demonstrate the superiority of the proposed model when compared to currently best-performing methods. By developing this innovative framework, our research aims to contribute significantly to the advancement of automatic video analysis, specifically in the domains of moment retrieval and highlight detection. The incorporation of natural language queries alongside the ability to address dynamic background changes represents a cutting-edge approach that effectively bridges the gap between user demands and video analysis tasks.