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<!DOCTYPE html>
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<h1 class="title is-1 publication-title">PMAA: A Progressive Multi-scale Attention Autoencoder Model for
High-Performance Cloud Removal from Multi-temporal Satellite Imagery</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://github.com/XavierJiezou" target="_blank">Xuechao Zou</a><sup>1,*</sup>,</span>
<span class="author-block">
<a href="https://cslikai.cn/" target="_blank">Kai Li</a><sup>2,*</sup>,</span>
<span class="author-block">
<a href="https://www.cs.tsinghua.edu.cn/info/1116/5088.htm" target="_blank">Junliang
Xing</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://www.cs.tsinghua.edu.cn/info/1117/3542.htm" target="_blank">Pin
Tao</a><sup>1,2,†</sup>,</span>
<span class="author-block">
<a href="https://cs.qhu.edu.cn/jxgz/jxysz/szgk/22173.htm" target="_blank">Yachao Cui</a><sup>1 </sup>
<span class="author-block">
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Qinghai University, <sup>2</sup>Tsinghua University<br>ECAI
2023</span>
<!--<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>-->
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<span>arXiv</span>
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</section>
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<!--/ Matting. -->
<!-- <h2 class="subtitle has-text-centered">
<span class="dnerf">ACR</span> is the first one-stage arbitrary hand reconstruction method using only a monocular RGB image as input.
</h2>
</div> -->
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<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud
cover significantly impedes its application. Although existing deep cloud removal models have achieved
notable outcomes, they scarcely consider contextual information. This study introduces a high-performance
cloud removal architecture, termed Progressive Multi-scale Attention Autoencoder (PMAA), which
concurrently harnesses global and local information to construct robust contextual dependencies using a
novel Multi-scale Attention Module (MAM) and a novel Local Interaction Module (LIM). PMAA establishes
long-range dependencies of multi-scale features using MAM and modulates the reconstruction of fine-grained
details utilizing LIM, enabling simultaneous representation of fine- and coarse-grained features at the
same level. With the help of diverse and multi-scale features, PMAA consistently outperforms the previous
state-of-the-art model CTGAN on two benchmark datasets. Moreover, PMAA boasts considerable efficiency
advantages, with only 0.5\% and 14.6\% of the parameters and computational complexity of CTGAN,
respectively. These comprehensive results underscore PMAA's potential as a lightweight cloud removal
network suitable for deployment on edge devices to accomplish large-scale cloud removal tasks. Our source
code and pre-trained models are available at <a
href="https://github.com/XavierJiezou/PMAA">https://github.com/XavierJiezou/PMAA</a>
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Method -->
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<h2 class="title is-3">Method Overview</h2>
<div class="content has-text-justified">
<center>
<table align=center width=850px>
<tr>
<td width=850px>
<center>
<img class="round" style="width:850px" src="image/README/pmaa.png" />
</center>
</td>
</tr>
</table>
<table align=center width=850px>
<tr>
<td>
Overview of our proposed high-performance cloud removal autoencoder. In the encoder, we downsample
the input image $N$ times. Then, the multi-scale features are fused by averaging pooling and
summation operations. A simplified transformer layer processes the fused features to obtain global
attention, which is used to modulate the multi-scale features. In the reconstruction process
(decoder), we use the local interaction module to recover more details.
</td>
</tr>
</table>
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<h2 class="subtitle has-text-centered">
Qualitative comparison with on InterHand 2.6M test dataset. Our approach generates better results in
two-hand reconstruction, particularly in challenging cases such as external occlusion (1), truncation
(3-4), or bending one finger with another hand (6). More results can be found in the Supplementary
Material.
</h2>
</div> -->
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Results from RGB2Hands dataset and in-the-wild videos.
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Ego-view results from RGB2Hands dataset and in-the-wild videos.
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Interacting hand and single hand reconstruction. Here, the images in (e) are selected from RGB2Hands benchmark. The others are from web videos.
</h2>
</div>
<div class="item"> -->
<!-- Your image here -->
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Hand-object interaction on web videos (watch videos above for more details).
</h2>
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</section> -->
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<h2 class="title is-3">Applications</h2>
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zou2023pmaa,
title={PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery},
author={Zou, Xuechao and Li, Kai and Xing, Junliang and Tao, Pin and Cui, Yachao},
journal={European Conference on Artificial Intelligence (ECAI)},
year={2023}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
<!--Acknowledgements-->
<section class="section" id="Acknowledgements">
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<h2 class="title">Acknowledgements</h2>
<p>
This work was supported in part by the Natural Science Foundation of China under Grant No. 62222606 and 62076238, in part by the Research on Efficiency Design of 3D Virtual Interactive Scene (k992146), and in part by the Research Foundation of the Key Laboratory of Spaceborne Information Intelligent Interpretation.
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