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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Diffusion Models as Masked Autoencoder">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>DiffMAE</title>
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table {
border-collapse: separate;
border-spacing: 3px;
margin: 0 auto;
}
caption {
padding: 10px;
caption-side: bottom;
font-weight: 1000;
}
</style>
</head>
<body>
<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">
Diffusion Models as Masked Autoencoders
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://weichen582.github.io/">Chen Wei</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://karttikeya.github.io/">Karttikeya Mangalam</a><sup>1</sup>,</span>
<span class="author-block">
<a href="http://www.cs.cmu.edu/~poyaoh/">Po-Yao Huang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://lyttonhao.github.io/">Yanghao Li</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://haoqifan.github.io/">Haoqi Fan</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://howardhsu.github.io//">Hu Xu</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://csrhddlam.github.io/">Huiyu Wang</a><sup>1</sup>
</span>
<span class="author-block">
<a href="https://cihangxie.github.io/">Cihang Xie</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://www.cs.jhu.edu/~ayuille/">Alan Yuille</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://feichtenhofer.github.io/">Christoph Feichtenhofer</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>FAIR, Meta AI</span>,
<span class="author-block"><sup>2</sup>Johns Hopkins University</span>,
<span class="author-block"><sup>3</sup>UC Santa Cruz</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2304.03283.pdf"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2304.03283"
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>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<div class="column" style="padding: 1px">
<img src="./diffmae/images/teaser-1.gif">
<table width="100%">
<tr>
<th width="33.3%">ground-truth</th>
<th width="33.3%"><span class="diffmae">DiffMAE</span></th>
<th width="33.3">MAE</th>
</tr>
</table>
</div>
<div class="column" style="padding: 1px">
<img src="./diffmae/images/teaser-2.gif">
<table width="100%">
<tr>
<th width="33.3%">ground-truth</th>
<th width="33.3%"><span class="diffmae">DiffMAE</span></th>
<th width="33.3">MAE</th>
</tr>
</table>
</div>
</div>
<h2 class="subtitle has-text-centered">
<span class="diffmae">DiffMAE</span> gradually adds visual details by diffusion for masked autoencoding.
</h2>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3"><span class="diffmae">DiffMAE</span></h2>
<div class="content has-text-justified">
<p>
It has been a longstanding belief that generation can facilitate a true understanding
of visual data.
</p>
<p>
In line with this, we revisit generatively pre-training visual representations
in light of denoising diffusion models, and build connection between
diffusion models and masked autoencoders.
</p>
<p>
In particular, we condition diffusion models on masked input and formulate diffusion models
as masked autoencoders (<span class="diffmae">DiffMAE</span>).
Our approach can:
<ul>
<li>Serve as a strong initialization for downstream <em>recognition</em> tasks;</li>
<li>Conduct <em>generative</em> image inpainting;</li>
<li>Be effortlessly extended to <em>video</em>.</li>
</ul>
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<!-- <h2 class="title is-3">Inpainting</h2> -->
<!-- Random Masking. -->
<h3 class="title is-4">Random Mask Inpainting</h3>
<div class="content has-text-justified has-text-centered">
<p>
The images are from ImageNet-1K validation set.
</p>
<p>
Use the slider to see generations from different inference steps.
</p>
</div>
<!-- Interpolating. -->
<div class="columns is-vcentered is-centered interpolation-panel">
<div class="column is-3 has-text-centered">
<img src="./diffmae/interpolation/input.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>masked input</p>
</div>
<div class="column is-3 is-centered">
<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="2" value="0" type="range">
</div>
<div class="column is-3 has-text-centered">
<img src="./diffmae/interpolation/gt.png"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">ground-truth</p>
</div>
</div>
<br/>
<!--/ Interpolating. -->
<div class="content has-text-justified has-text-centered">
<p>
Hover to view the masked inputs.
</p>
</div>
<div class="columns">
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/random-0-2.png" class="src-image">
<img src="./diffmae/images/random-0-1.png" class="dst-image">
</div>
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/random-1-2.png" class="src-image">
<img src="./diffmae/images/random-1-1.png" class="dst-image">
</div>
</div>
<div class="columns">
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/random-2-2.png" class="src-image">
<img src="./diffmae/images/random-2-1.png" class="dst-image">
<table width="100%">
<tr>
<td width="50%">ground-truth</th>
<td width="50%"><span class="diffmae">DiffMAE</span></th>
</tr>
</table>
</div>
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/random-3-2.png" class="src-image">
<img src="./diffmae/images/random-3-1.png" class="dst-image">
<table width="100%">
<tr>
<td width="50%">ground-truth</th>
<td width="50%"><span class="diffmae">DiffMAE</span></th>
</tr>
</table>
</div>
</div>
<!--/ Random Masking. -->
<!-- Center Masking. -->
<h3 class="title is-4">Center Mask Inpainting</h3>
<div class="content has-text-justified has-text-centered">
<p>
The images are from ImageNet-1K validation set.
Hover to view the masked inputs.
</p>
</div>
<div class="columns">
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/center-0-2.png" class="src-image" width="94%" height="94%" >
<img src="./diffmae/images/center-0-1.png" class="dst-image" width="94%" height="94%">
</div>
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/center-1-2.png" class="src-image" width="94%" height="94%">
<img src="./diffmae/images/center-1-1.png" class="dst-image" width="94%" height="94%">
</div>
</div>
<div class="columns">
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/center-2-2.png" class="src-image" width="94%" height="94%">
<img src="./diffmae/images/center-2-1.png" class="dst-image" width="94%" height="94%">
<table width="100%">
<tr>
<td width="50%">ground-truth</th>
<td width="50%"><span class="diffmae">DiffMAE</span></th>
</tr>
</table>
</div>
<div class="column overlay-image has-text-centered">
<img src="./diffmae/images/center-3-2.png" class="src-image" width="94%" height="94%">
<img src="./diffmae/images/center-3-1.png" class="dst-image" width="94%" height="94%">
<table width="100%">
<tr>
<td width="50%">ground-truth</th>
<td width="50%"><span class="diffmae">DiffMAE</span></th>
</tr>
</table>
</div>
</div>
<!--/ Center Masking. -->
<!-- Video Inpainting. -->
<h3 class="title is-4">Video Inpainting</h3>
<div class="content has-text-justified has-text-centered">
<p>
The videos are from Kinetics-400 validation set.
</p>
</div>
<div class="columns">
<div class="column has-text-centered">
<img src="./diffmae/images/video-0.gif" width="95%" height="95%" >
<table width="100%">
<tr>
<td width="33.3%">ground-truth</td>
<td width="33.3%">inputs</td>
<td width="33.3"><span class="diffmae">DiffMAE</span></td>
</tr>
</table>
</div>
<div class="column has-text-centered">
<img src="./diffmae/images/video-1.gif" width="95%" height="95%">
<table width="100%">
<tr>
<td width="33.3%">ground-truth</td>
<td width="33.3%">inputs</td>
<td width="33.3"><span class="diffmae">DiffMAE</span></td>
</tr>
</table>
</div>
</div>
<!--/ Video Inpainting. -->
<!-- Fine-Tuning. -->
<h3 class="title is-4">Fine-Tuning Generative Models</h3>
<div class="content has-text-justified has-text-centered">
<p>
While being able to generatively inpaint images,
<strong><span class="diffmae">DiffMAE</span></strong> is a strong
self-supervised pre-training approach. The performance is:
<ul>
<li>Comparable to leading self-supervised algorithms that focus solely on recognition;</li>
<li>Stronger than other generative based algorithms by a large margin.</li>
</ul>
</p>
</div>
<div class="is-centered has-text-centered">
<table style="width: 70%">
<tr>
<th>pre-train</th>
<th>architecture</th>
<th>params. (M)</th>
<th>fine-tuned</th>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid #ddd;"></td>
</tr>
<tr>
<td style="color:lightgray">MAE</td>
<td style="color:lightgray">ViT-L</td>
<td style="color:lightgray">304</td>
<td style="color:lightgray">85.9</td>
</tr>
<tr>
<td colspan="4" style="border-bottom: 1px solid #ddd;"></td>
</tr>
<tr>
<td>iGPT</td>
<td>iGPT-L</td>
<td>1362</td>
<td>72.6</td>
</tr>
<tr>
<td>ADM</td>
<td>U-Net</td>
<td>211</td>
<td>83.3</td>
</tr>
<tr>
<td>DDPM</td>
<td>ViT-L</td>
<td>304</td>
<td>83.4</td>
</tr>
<tr>
<td><strong><span class="diffmae">DiffMAE</span></strong></td>
<td>ViT-L</td>
<td>304</td>
<td><strong>85.8</strong></td>
</tr>
<caption>
Fine-tuning generative models on ImageNet-1K, a system-level comparison.
</caption>
</table>
</div>
</div>
<!--/ Fine-Tuning. -->
<br/>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{wei2023diffusion,
author = {Wei, Chen and Mangalam, Karttikeya and Huang, Po-Yao and Li, Yanghao and Fan, Haoqi and Xu, Hu and Wang, Huiyu and Xie, Cihang and Yuille, Alan and Feichtenhofer, Christoph},
title = {Diffusion Models as Masked Autoencoder},
booktitle = {ICCV},
year = {2023},
}</code></pre>
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