Efficient retrieval of specific elements within long-form video content presents significant challenges in multimedia information processing. This paper introduces AMS (Adaptive Multi-modal Search), a novel framework that seamlessly integrates semantic feature fusion and multi-modal Retrieval-Augmented Generation (RAG) for comprehensive video content understanding and retrieval. Our approach addresses the fundamental limitations in existing video search systems, particularly for extended-duration content, by implementing a hierarchical cross-modal architecture that effectively processes and aligns visual, auditory, and contextual information. The proposed framework incorporates three key innovations: (1) a fine-grained semantic fusion mechanism that dynamically integrates character information, scene context, and dialogue content; (2) an adaptive multi-modal RAG system that generates detailed scene descriptions while maintaining temporal coherence; and (3) a hierarchical embedding structure that enables precise temporal localization of query-relevant content within extensive video sequences. Experimental results on [Dataset Name] demonstrate that our approach achieves state-of-the-art performance, with a [X%] improvement in retrieval accuracy and a [Y%] reduction in search latency compared to existing methods. The system exhibits robust performance across diverse query types, including visual content, character interactions, plot elements, and dialogue retrieval. Furthermore, our framework demonstrates exceptional scalability, maintaining high precision even with videos exceeding [Z] hours in duration. This work represents a significant advancement in video content retrieval, offering practical solutions for applications in media production, content management, and video analytics. The proposed methodology establishes a new paradigm for handling complex, long-form video content while maintaining computational efficiency and retrieval accuracy.
AMS able to do person couple with action query on whole video (up to 6 hours)
Efficiency Improvement with AMS
| Workflow Steps | Traditional Method | AMS Solution | Efficiency Gain |
|---|---|---|---|
| Content Search | Manual scanning through video timeline (30-60 mins) | Instant semantic search with precise timestamp (5-10 seconds) | ↓ 98% time |
| Scene Analysis | Manual review and note-taking (45-90 mins) | Automated multi-modal understanding with character and plot detection (instant) | ↓ 99% time |
| Clip Extraction | Manual trimming and exporting (15-30 mins) | Precise timestamp-based extraction (2-3 seconds) | ↓ 95% time |
| AI Integration | Limited or no AI support |
- Direct integration with editing AI agents - Automated post-production - Smart content recommendations |
New capability |
| Workflow Automation | Multiple manual steps and tools |
- One-click search and extract - Automated scene tagging - Batch processing support |
↓ 90% complexity |
| Scalability | Linear time increase with video length | Constant search time regardless of video length | Exponential improvement |
* Results based on average processing time for 2-hour video content
@article{Fagang,
title={Bumblebee: Advancing Beyond Closed-Source Multi-Modal Models through Token Shrinkage},
author={Fagang Jin, Chen Tong, Lin You},
year={2024},
primaryClass={cs.CV}
}