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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-67835289-3"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-67835289-3');
</script>
<!-- ======================================================================= -->
<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript">google.load("jquery", "1.3.2");</script>
<style type="text/css">
body {
/* font-family: "Titillium Web","HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif; */
font-family: "Avenir", sans-serif;
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</style>
<html>
<head>
<title>Space-Time Correspondence as a Contrastive Random Walk</title>
<meta property="og:title" content="Space-Time Correspondence as a Contrastive Random Walk " />
<meta property="og:image" content="" />
<meta property="og:image:width" content="3000" />
<meta property="og:image:height" content="800" />
</head>
<body>
<br><br><br><br>
<center>
<span style="font-size:38px">Space-Time Correspondence as a Contrastive Random Walk</span>
<br><br>
<table align=center width=600px>
<tr>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="http://ajabri.github.io">Allan A. Jabri</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="http://www.andrewowens.com">Andrew Owens</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="http://www.eecs.berkeley.edu/~efros/">Alexei A. Efros</a></span>
</center>
</td>
</table>
<table align=center width=650px style="font-size:20px;">
<tr>
<td align=center width=150px>
<center>
<span><a href="https://proceedings.neurips.cc/paper/2020/hash/e2ef524fbf3d9fe611d5a8e90fefdc9c-Abstract.html"> [Paper]</a></span>
</center>
</td>
<td align=center width=150px>
<center>
<span style=""><a href="http://www.github.com/ajabri/videowalk"> [Code]</a></span>
</center>
</td>
<td align=center width=150px>
<center>
<span style=""><a href="https://youtu.be/UaOcjxrPaho"> [Talk]</a></span>
</center>
</td>
<td align=center width=150px>
<center>
<span style=""><a href="https://www.dropbox.com/s/snpj68cssu3b4to/jabri.neurips2020.poster.pdf"> [Poster]</a></span>
</center>
</td>
<td align=center width=150px>
<center>
<span style=""><a href="https://www.dropbox.com/s/qrqb0ssjlh1tph1/jabri.nips.12min.public.key"> [Slides]</a></span>
</center>
</td>
</tr>
<tr>
</table>
<br>
<span style="font-size:28px">NeurIPS 2020</span>
</center>
<br><br>
<table align=center width=850px>
<tr>
<td width=400px>
<center>
<a href="figs/teaser_animation.gif"><img src = "figs/teaser_animation.gif" width="90%"></img></href></a><br>
</center>
</td>
</tr>
</table>
<br><br>
<hr>
<table align=center>
<div class='row'>
<div class='column'>
<center>
<h1>Abstract</h1>
<p style="width:560px; text-align:left; color:darkslategray; ;">This paper proposes a simple self-supervised approach for learning representations
for visual correspondence from raw video. We cast correspondence as link prediction in a space-time graph constructed from a video. In this graph, the nodes
are patches sampled from each frame, and nodes adjacent in time can share a
directed edge. We learn a node embedding in which pairwise similarity defines
transition probabilities of a random walk. Prediction of long-range correspondence
is efficiently computed as a walk along this graph. The embedding learns to guide
the walk by placing high probability along paths of correspondence. Targets are
formed without supervision, by cycle-consistency: we train the embedding to maximize the likelihood of returning to the initial node when walking along a graph
constructed from a 'palindrome' of frames. We demonstrate that the approach allows
for learning representations from large unlabeled video. Despite its simplicity,
the method outperforms the self-supervised state-of-the-art on a variety of label
propagation tasks involving objects, semantic parts, and pose. Moreover, we show
that self-supervised adaptation at test-time and edge dropout improve transfer for
object-level correspondence.</p>
</center>
</div>
<div class="column" style='float:right; width:40%'>
<center>
<h1>Pseudo-code</h1>
<!-- <embed src="figs/pseudocode.pdf" width="800px" height="2100px" /> -->
<img src = "figs/pseudocode.png" width="90%"></img>
</center>
</div>
</div>
</table>
<br><br>
</table>
<hr>
<center>
<h1>Talk</h1>
<table border="0" align="center" cellspacing="0" cellpadding="20">
<td align="center" valign="middle">
<iframe width="800" height="450" src="https://www.youtube.com/embed/UaOcjxrPaho" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</td>
</table>
</center>
<hr>
<center>
<h1>More Qualitative Results</h1>
<table border="0" align="center" cellspacing="0" cellpadding="20">
<td align="center" valign="middle">
<iframe width="800" height="450" src="https://www.youtube.com/embed/R_Zae5N_hKw" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</td>
</table>
</center>
<hr>
<!-- <table align=center width=550px> -->
<table align=center width=800px>
<center><h1>Paper</h1></center>
<tr>
<td><a href=""><img class="layered-paper-big" style="height:175px" src="./figs/firstpage.png"/></a></td>
<td><span style="font-size:14pt">Allan Jabri, Andrew Owens, Alexei A. Efros.<br>
<i><a href="https://proceedings.neurips.cc/paper/2020/hash/e2ef524fbf3d9fe611d5a8e90fefdc9c-Abstract.html">Space-Time Correspondence as a Contrastive Random Walk.</a></i><br>
NeurIPS 2020, Oral Presentation.<br>
</a>
<span style="font-size:4pt"><a href=""><br></a>
</span>
</td>
</td>
</tr>
</table>
<br>
<table align=center width=600px>
<tr>
<td><span style="font-size:14pt"><center>
<a href="./figs/bibtext.txt">[Bibtex]</a>
</center></td>
</tr>
</table>
<hr>
<table align=center width=1100px>
<tr>
<td width=400px>
<left>
<center><h1>Acknowledgements</h1></center>
We thank Amir Zamir, Ashish Kumar, Tim Brooks, Bill Peebles, Dave Epstein,
Armand Joulin, and Jitendra Malik for very helpful feedback. We are also grateful to the wonderful
members of VGG for hosting us during a dreamy semester at Oxford. This work would not
have been possible without the hospitality of Port Meadow and the swimming pool on Iffley Road.
Research was supported, in part, by NSF grant IIS-1633310, the DARPA MCS program, and NSF
IIS-1522904. AJ is supported by the PD Soros Fellowship.
</table>
<br><br>
</table>
</left>
</td>
</tr>
</table>
<br><br>
</body>
</html>