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hkchengrex committed Nov 19, 2020
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/* Custom Stylesheet */
/**
* Use this file to override Materialize files so you can update
* the core Materialize files in the future
*
* Made By MaterializeCSS.com
*/

.icon-block {
padding: 0 15px;
}
.icon-block .material-icons {
font-size: inherit;
}
137 changes: 137 additions & 0 deletions docs/index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1.0"/>
<title>CascadePSP Project Website</title>

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<br><br>
<h5 class="header center orange-text">CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement</h5>
<h6 class="header center"><i>CVPR 2020</i></h6>
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<h5 class="header col s12 light">Ho Kei Cheng*, Jihoon Chung*, Yu-Wing Tai, Chi-Keung Tang</h5>
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<div class="row center">
<div class="col s12 center">
<a href="https://arxiv.org/abs/2005.02551" target="_blank"
class="btn-large waves-effect waves-light green lighten-4 black-text">Paper</a>
&nbsp;&nbsp;&nbsp;
<a href="https://github.com/hkchengrex/CascadePSP" target="_blank"
class="btn-large waves-effect waves-light green lighten-4 black-text">Github</a>
&nbsp;&nbsp;&nbsp;
<a href="https://github.com/hkchengrex/CascadePSP/tree/master/segmentation-refinement" target="_blank"
class="btn-large waves-effect waves-light green lighten-4 black-text">Quick Start</a>
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<img class="materialboxed" width="100%" src="images/teaser.jpg">
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<h5 class="header center">Abstract</h1>
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<p style="text-align: justify;">
State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.
</p>
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<h5 class="header center">Citation</h1>

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<pre style="background-color: aliceblue;overflow:auto;padding: 10px;">
@inproceedings{CascadePSP2020,
title={CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement},
author={Cheng, Ho Kei and Chung, Jihoon and Tai, Yu-Wing and Tang, Chi-Keung},
booktitle={CVPR},
year={2020}
}</pre>
</div>
</div>




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<h5 class="white-text">Contact</h5>
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Please Email Ho Kei Cheng (<a href = "mailto: [email protected]" style="color:#a6d9fc;">[email protected]</a>) for any questions.
</p>

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<a href="https://github.com/hkchengrex/CascadePSP/blob/master/LICENSE" style="color:white">LICENSE</a>
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<script>
document.addEventListener('DOMContentLoaded', function() {
var elems = document.querySelectorAll('.materialboxed');
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});

// Or with jQuery

$(document).ready(function(){
$('.materialboxed').materialbox();
});
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(function($){
$(function(){

$('.sidenav').sidenav();

}); // end of document ready
})(jQuery); // end of jQuery name space
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