Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets

TL;DR

Fusionista2.0 is an optimized video retrieval system that significantly improves speed and usability for large-scale datasets, achieving faster retrieval times and higher user satisfaction.

Contribution

The paper introduces a re-engineered, efficient video retrieval system with technical upgrades and a redesigned interface, enhancing performance and user experience.

Findings

Retrieval time reduced by up to 75%.

Accuracy and user satisfaction increased.

System is suitable for large-scale video search.

Abstract

The Video Browser Showdown (VBS) challenges systems to deliver accurate results under strict time constraints. To meet this demand, we present Fusionista2.0, a streamlined video retrieval system optimized for speed and usability. All core modules were re-engineered for efficiency: preprocessing now relies on ffmpeg for fast keyframe extraction, optical character recognition uses Vintern-1B-v3.5 for robust multilingual text recognition, and automatic speech recognition employs faster-whisper for real-time transcription. For question answering, lightweight vision-language models provide quick responses without the heavy cost of large models. Beyond these technical upgrades, Fusionista2.0 introduces a redesigned user interface with improved responsiveness, accessibility, and workflow efficiency, enabling even non-expert users to retrieve relevant content rapidly. Evaluations demonstrate that retrieval time was reduced by up to 75% while accuracy and user satisfaction both increased, confirming Fusionista2.0 as a competitive and user-friendly system for large-scale video search.