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<!DOCTYPE html>
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<h1 class="title is-1 publication-title">LEMA: LMT-oriented Entity Mapping Network for Long-text Composed Image Retrieval</h1>
<div class="is-size-5 publication-authors">
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<!-- <a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Zhiwei Chen<sup>1</sup></a>,</span> -->
<span class="author-block" style="font-weight: 1000;">
<span>Zixu Li<sup>1</sup></span>,
</span>
<span class="author-block" style="font-weight: 1000;">
<span>Zhiheng Fu<sup>1</sup></span>,
</span>
<span class="author-block" style="font-weight: 1000;">
<span>Yupeng Hu<sup>1</sup></span>,
</span>
<span class="author-block" style="font-weight: 1000;">
<span>Zhiwei Chen<sup>1</sup></span>,
</span>
<span class="author-block" style="font-weight: 1000;">
<span>Xuemeng Song<sup>1</sup></span>,
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<span class="author-block" style="font-weight: 1000;">
<span>Weili Guan<sup>2</sup></span>,
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<span class="author-block"><sup>1</sup>Shandong University,<sup>2</sup>Harbin Institute of Technology (Shenzhen)<br>Date: 2024-11</span>
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<span>L-CIR Dataset</span>
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<h2 class="title is-3">Long-text Composed Image Retrieval (L-CIR)</h2>
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<!-- Your image here -->
<img src="static/images/Intro.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Examples of (a) traditional CIR and (b) our proposed L-CIR. Their primary distinction lies in the length of the modification text.
</h2>
</div>
</div>
</div>
<br>
<div class="item">
<!-- Your image here -->
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<div class="column is-three-fifths">
<img src="static/images/PerformanceComparison.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Performance comparison of representative CIR baselines on short and long modification text datasets.
</h2>
</div>
<div class="column is-two-fifths">
<img src="static/images/One2Many.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Example of the One-to-Many correspondence in L-CIR.
</h2>
</div>
</div>
</div>
<br>
<hr>
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<!-- Your image here -->
<h2 class="title is-3">L-CIR Datasets Construction</h2>
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<div class="column is-two-fourth">
<img src="static/images/DataProcess.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Process of our BLIP-based LMT generation.
</h2>
</div>
<div class="column is-two-fourth">
<img src="static/images/DataStatic.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
</h2>
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</div>
<br>
<div class="item">
<!-- Your image here -->
<img src="static/images/Prompts.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Generated LMTs using various prompts for BLIP-3
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<h2 class="subtitle has-text-centered">
The mitigation on the false-negative samples when using LMT. We showed the top-5 retrieved results on both L-CIR and CIR. The target images are framed in green.
</h2>
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<br>
<hr>
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<h2 class="title is-3">Framework: LMT-oriented Entity Mapping Network (LEMA)</h2>
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Overall architecture of our proposed LEMA.
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<hr>
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</section>
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<h2 class="title is-3">Experiment</h2>
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<img src="static/images/Sensitivity.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Sensitivity results of the hyper-parameter κ on L-CIRR and the hyper-parameter μ on both L-FashionIQ and L-CIRR.
</h2>
</div>
<br>
<div class="item">
<!-- Your image here -->
<img src="static/images/Case.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
The qualitative results for the PA module, which shows the LMT and corresponding summary. The to-be-modified entities are colored.
</h2>
</div>
<br>
<div class="item">
<!-- Your image here -->
<img src="static/images/Attention.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Attention visualization results for the reference image on L-FashionIQ by the PA-generated summary.
</h2>
</div>
<br>
<div class="item">
<!-- Your image here -->
<img src="static/images/Attention_CIRR.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Attention visualization results for the reference image on L-CIRR by the PA-generated summary.
</h2>
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<br>
<!-- </div> -->
</div>
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