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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension.">
<meta name="keywords" content="Retrieval-Augmented, Caption, Open-World Comprehension">
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<title>EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension</title>
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<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title" style="font-size: 32px; line-height: 42px;">
<span style="font-size: 38px; display: block; margin-bottom: 8px;"><b>EVCap</b>:</span>
Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://jiaxuan-li.github.io/">Jiaxuan Li</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="https://vmdlab.github.io/">MinhDuc Vo</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="http://research.nii.ac.jp/~sugimoto/index.html">Akihiro Sugimoto</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="http://www.nlab.ci.i.u-tokyo.ac.jp/index-e.html">Hideki Nakayama</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>The University of Tokyo,</span>
<span class="author-block"><sup>2</sup>National Institute of Informatics</span>
</div>
<div class="is-size-8 publication-authors">
<span >*equal contribution</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><strong>CVPR 2024</strong></span>
</div>
<br>
<div class="column has-text-centered">
<div class="publication-links">
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<span class="link-block">
<a href="https://arxiv.org/pdf/2311.15879.pdf"
class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Paper</span>
</a>
</span>
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<a href="https://arxiv.org/abs/2311.15879"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/Jiaxuan-Li/EVCap"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<span class="link-block">
<a
class="external-link button is-normal is-rounded is-dark">
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<i class="far fa-images"></i>
</span>
<span>Data</span>
</a>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Large language models (LLMs)-based image captioning has the capability of describing objects
not explicitly observed in training data; yet novel objects occur frequently, necessitating
the requirement of sustaining up-to-date object knowledge for open-world comprehension.
</p>
<p>
Instead of relying on large amounts of data and scaling up network parameters,
we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs
with object names retrieved from External Visual--name memory (EVCap).
We build ever-changing object knowledge memory using objects' visuals and names, enabling us to
(i) update the memory at a minimal cost and
(ii) effortlessly augment LLMs with retrieved object names by utilizing a lightweight and fast-to-train model.
Our model, which was trained only on the COCO dataset,
can adapt to out-of-domain without requiring additional fine-tuning or re-training.
</p>
<p>
Our comprehensive experiments conducted on various benchmarks and synthetic commonsense-violating data demonstrate that EVCap,
comprising solely 3.97M trainable parameters, exhibits superior performance compared to other methods of equivalent model size scale.
Notably, it achieves competitive performance against specialist SOTAs with an enormous number of parameters.
</p>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<!-- Visual Effects. -->
<div class="column">
<div class="content">
<h2 class="title is-3">Model</h2>
<div class="content has-text-justified">
<p>
EVCap consists of an external visual-name memory with image embeddings and object names (upper), a frozen ViT and Q-Former equipped with trainable image query tokens, an attentive fusion module developed by a customized
frozen Q-Former and trainable object name query tokens, and a frozen LLM with a trainable linear layer (lower).
</p>
</div>
<img src="./static/images/model.png"
class="interpolation-image"
alt="Interpolate start reference image."
width="75%"/>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{li2024evcap,
title={EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension},
author={Jiaxuan Li and Duc Minh Vo and Akihiro Sugimoto and Hideki Nakayama},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
}</code></pre>
</div>
</section>
<section class="section">
<div class="container is-max-desktop content">
<h2 class="title">Acknowledgement</h2>
<p>
This website is adapted from <a href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>, licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
</div>
</section>
</body>
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