Video-based bug reports have become a promising alternative to text-based reports for programs centered around a graphical user interface (GUI), as they allow for seamless documentation of software faults by visually capturing buggy behavior on app screens. However, developing automated techniques to manage video-based reports is challenging as it requires identifying and understanding often nuanced visual patterns that capture key information about a reported bug. Therefore, my research endeavors to overcome these challenges by advancing the bug report management task of duplicate detection for video-based reports. The objectives of my research are fourfold: (i) investigate the benefits of tailoring recent advancements in the computer vision domain for learning both visual and textual patterns from video frames depicting GUI screens to detect duplicate reports; (ii) adapt the scene-learning capabilities of vision transformers to capture subtle visual and textual patterns that manifest on app UI screens; (iii) construct a more comprehensive and realistic benchmark which contains video-based bug reports derived from real bugs; (iv) conduct an empirical evaluation to potentially demonstrate state-of-the-art improvements achieved by the proposed approach.