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<title>AMS: A Hierarchical Cross-Modal Transformer Architecture with Adaptive Fusion for Fine-grained Video Element
Understanding and Retrieval</title>
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<h1 class="title is-1 publication-title">AMS: A Hierarchical Cross-Modal Transformer Architecture with
Adaptive Fusion for Fine-grained Video Element Understanding and Retrieval</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Fagang Jin</a><sup>*</sup>,</span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Shen Wei</a><sup>*</sup>,</span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Tong Ye</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Anonymous<br>2024</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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pretium mi. Maecenas dignissim tincidunt vestibulum. Sed consequat hendrerit nisl ut maximus.
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
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 VSD (VarityShow Dataset) demonstrate that our approach achieves state-of-the-art
performance, with a 40% improvement in retrieval accuracy and a 80% 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 6 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.
</p>
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<video src="static/AMSSearch.mp4" alt="MY ALT TEXT" autoplay muted loop controls></video>
<p class="is-size-6 has-text-centered">
AMS able to do person couple with action query on whole video (up to 6 hours)
</p>
</div>
</div>
</div>
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</div>
</section>
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<div class="content has-text-justified">
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<p class="is-size-6 has-text-centered">
Efficiency Improvement with AMS
</p>
<table class="table is-fullwidth is-bordered mt-4">
<thead>
<tr>
<th>Workflow Steps</th>
<th>Traditional Method</th>
<th>AMS Solution</th>
<th>Efficiency Gain</th>
</tr>
</thead>
<tbody>
<tr>
<td>Content Search</td>
<td>Manual scanning through video timeline (30-60 mins)</td>
<td>Instant semantic search with precise timestamp (5-10 seconds)</td>
<td><strong>↓ 98% time</strong></td>
</tr>
<tr>
<td>Scene Analysis</td>
<td>Manual review and note-taking (45-90 mins)</td>
<td>Automated multi-modal understanding with character and plot detection (instant)</td>
<td><strong>↓ 99% time</strong></td>
</tr>
<tr>
<td>Clip Extraction</td>
<td>Manual trimming and exporting (15-30 mins)</td>
<td>Precise timestamp-based extraction (2-3 seconds)</td>
<td><strong>↓ 95% time</strong></td>
</tr>
<tr>
<td>AI Integration</td>
<td>Limited or no AI support</td>
<td>
- Direct integration with editing AI agents<br>
- Automated post-production<br>
- Smart content recommendations
</td>
<td><strong>New capability</strong></td>
</tr>
<tr>
<td>Workflow Automation</td>
<td>Multiple manual steps and tools</td>
<td>
- One-click search and extract<br>
- Automated scene tagging<br>
- Batch processing support
</td>
<td><strong>↓ 90% complexity</strong></td>
</tr>
<tr>
<td>Scalability</td>
<td>Linear time increase with video length</td>
<td>Constant search time regardless of video length</td>
<td><strong>Exponential improvement</strong></td>
</tr>
</tbody>
</table>
<p class="is-size-7 has-text-centered mt-3">
* Results based on average processing time for 5-hour video content
</p>
</div>
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<h2 class="title">BibTeX</h2>
<pre>
<code>
@article{Fagang,
title={AMS: A Hierarchical Cross-Modal Transformer Architecture with Adaptive Fusion for Fine-grained Video Element
Understanding and Retrieval},
author={Fagang Jin, Chen Tong, Lin You},
year={2024},
primaryClass={cs.CV}
}
</code></pre>
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AMS 2024
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