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<!DOCTYPE html>
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content="Generative Multimodal Models are In-Context Learners">
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<title>Generative Multimodal Models are In-Context Learners</title>
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<h1 class="title is-1 publication-title">Generative Multimodal Models are In-Context Learners</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/Quan-Sun">Quan Sun</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=5Ydha2EAAAAJ&hl=en">Yufeng Cui</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://zhangxiaosong18.github.io">Xiaosong Zhang</a><sup>1*</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=VsJ39HMAAAAJ">Fan Zhang</a><sup>1*</sup>,
</span>
<span class="author-block">
<a href="https://yqy2001.github.io">Qiying Yu</a><sup>2,1*</sup>,
</span>
<span class="author-block">
<a href="https://greatlog.github.io">Zhengxiong Luo</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Yueze Wang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://raoyongming.github.io">Yongming Rao</a><sup>1</sup>
</span>
<span class="author-block">
<a href="https://air.tsinghua.edu.cn/en/info/1046/1194.htm">Jingjing Liu</a><sup>2</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=knvEK4AAAAAJ&hl=en">Tiejun Huang</a><sup>1,3</sup>
</span>
<span class="author-block">
<a href="https://www.xloong.wang/">Xinlong Wang</a><sup>1†</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Beijing Academy of Artificial Intelligence</span>
<span class="author-block"><sup>2</sup>Tsinghua University</span>
<span class="author-block"><sup>3</sup>Peking University</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>*</sup>equal contribution</span>
<span class="author-block"><sup>†</sup>project lead</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2312.13286"
class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
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<a href="https://github.com/baaivision/Emu"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
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<!-- Demo Link. -->
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<a href="https://emu.ssi.plus"
class="external-link button is-normal is-rounded is-dark">
<span>Demo</span>
</a>
</span>
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<a href="https://huggingface.co/spaces/BAAI/Emu2"
class="external-link button is-normal is-rounded is-dark">
<span>🤗HF Demo</span>
</a>
</span>
<!-- Demo Link. -->
<span class="link-block">
<a href="https://huggingface.co/BAAI/Emu2"
class="external-link button is-normal is-rounded is-dark">
<span>🤗HF Model</span>
</a>
</span>
</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>
The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
In this work, we demonstrate that the task-agnostic in-context learning capabilities of large multimodal models can be significantly enhanced by effective scaling-up.
We introduce <b style="color: rgb(100,149,237);">Emu2</b>, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences with a unified autoregressive objective.
<b style="color: rgb(100,149,237);">Emu2</b> exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning, such as visual prompting and object-grounded generation.
The model sets a new record on multiple multimodal understanding tasks in few-shot settings.
When instruction-tuned to follow specific instructions, <b style="color: rgb(100,149,237);">Emu2</b> further achieves new state-of-the-art on challenging tasks such as question answering benchmarks for large multimodal models and open-ended subject-driven generation.
These achievements demonstrate that <b style="color: rgb(100,149,237);">Emu2</b> can serve as a base model and general-purpose interface for a wide range of multimodal tasks.
Code and models are publicly available to facilitate future research.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
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<h2 class="title is-3">Video</h2>
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<iframe width="560" height="315" src="https://www.youtube.com/embed/nz4BJ7MKtKo?si=j-aVv3ECpCG4FQwV"
title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen>
</iframe>
</div>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns">
<div class="column is-full-width">
<div class="columns is-centered has-text-centered">
<div class="column is-full-width">
<h2 class="title is-3">A strong multimodal few-shot learner</h2>
<div class="columns is-centered">
<img src="./static/images/comparison_fewshot.png" class="interpolation-image" alt="comparison_fewshot." width="80%" heght="80%" />
</div>
<h2 class="title is-3">An impressive multimodal generalist</h2>
<div class="columns is-centered">
<img src="./static/images/radar.png" class="interpolation-image" alt="Radar." height="50%" width="50%" />
</div>
<br>
<h2 class="title is-3">A skilled painter</h2>
<div class="columns is-centered">
<img src="./static/images/gen_compare_metrics.png" class="interpolation-image" alt="gen_metrics." height="30%" width="50%" style="position:relative;left:50px;"/>
</div>
<div class="content is-centered">
<p>
Zero-shot subject-driven generation
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns">
<div class="column is-full-width">
<div class="columns is-centered has-text-centered">
<div class="column is-full-width">
<!-- cases. -->
<h2 class="title is-3">Multimodal in-context learning</h3>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/multi-modal-incontext-learning.png" class="interpolation-image" alt="multi-modal-incontext-learning."/>
</div>
</div>
<h2 class="title is-3">Strong multimodal understanding</h3>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/hexogon.png" class="interpolation-image" alt="hexogon." width="80%" height="80%"/>
</div>
</div>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/guid_robot.png" class="interpolation-image" alt="guide robot." width="80%" height="80%"/>
</div>
</div>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/damage_car.png" class="interpolation-image" alt="damage car." width="80%" height="80%"/>
</div>
</div>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/sample_AB.png" class="interpolation-image" alt="sample A and B." width="80%" height="80%"/>
</div>
</div>
<h2 class="title is-3">Generate image from any prompt sequence</h3>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/bear.gif" class="interpolation-image" alt="generate_from_any_prompt_sequence1." width="80%" height="80%" />
</div>
</div>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/dog_emu.gif" class="interpolation-image" alt="generate_from_any_prompt_sequence2." width="80%" height="80%" />
</div>
</div>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/emu-g-case.jpg" class="interpolation-image" alt="generate_from_any_prompt_sequence3." width="80%" height="80%" />
</div>
</div>
<h2 class="title is-3">Generate video from any prompt sequence</h3>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/video.gif" class="interpolation-image" alt="video_generation." />
</div>
</div>
<div class="video-container">
<video width="200" height="200" autoplay controls muted loop playsinline>
<source src="./static/videos/blue_bird.mp4"
type="video/mp4">
</video>
<video width="200" height="200" autoplay controls muted loop playsinline>
<source src="./static/videos/floating.mp4"
type="video/mp4">
</video>
<video width="200" height="200" autoplay controls muted loop playsinline>
<source src="./static/videos/emu.mp4"
type="video/mp4">
</video>
<video width="200" height="200" autoplay controls muted loop playsinline>
<source src="./static/videos/2dogs.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns">
<!-- Paper method. -->
<div class="columns is-centered has-text-centered">
<div class="column is-full-width">
<h2 class="title is-3">Method</h2>
<div class="content has-text-justified">
<p>
<b style="color: rgb(100,149,237);">Emu2</b> learns with a predict-the-next-element objective in multimodality. Each image in the
multimodal sequence is tokenized into embeddings via a visual encoder, and then interleaved with text tokens for autoregressive modeling.
The regressed visual embeddings will be decoded into an image or a video by a visual decoder. Compared to Emu1, <b style="color: rgb(100,149,237);">Emu2</b> embraces a simpler framework,
better visual decoder, and scales up to 37 billion parameters.
</p>
<img src="./static/images/emu2_method.png" class="interpolation-image" alt="emu2_method."/>
</div>
</div>
</div>
<!--/ Paper method. -->
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{Emu2,
title={Generative Multimodal Models are In-Context Learners},
author={Quan Sun and Yufeng Cui and Xiaosong Zhang and Fan Zhang and Qiying Yu and Zhengxiong Luo and Yueze Wang and Yongming Rao and Jingjing Liu and Tiejun Huang and Xinlong Wang},
publisher={arXiv preprint arXiv:2312.13286},
year={2023}
}</code></pre>
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