<div dir="ltr">
<span>Apologies for cross-posting<br></span><div><span>*******************************</span></div><div><span><br></span></div><div><span></span></div><div><div><span><span>CALL</span></span> FOR <span><span>PAPERS</span></span>  & <span><span>CALL</span></span> FOR PARTICIPANTS IN 7 CHALLENGES</div><div><br></div><div>1<span>st Mobile AI <span>workshop and challenges on<span><span><br></span></span></span></span></div><div><span><span><span><span>learned ISP, denoising, HDR, image/video super-resolution, depth estimation, scene detection</span></span></span></span><br><span>In</span> conjunction with CVPR <span><span><span>2021</span></span></span>, June, Nashville, USA (VIRTUAL).</div><div><br></div><div>
<div><div><div>Website: 
<a href="https://ai-benchmark.com/workshops/mai/2021/">https://ai-benchmark.com/workshops/mai/2021/</a>

</div><div><br></div><div>TOPICS</div><div><br></div><div style="margin-left:40px">
●    Efficient deep learning models for mobile devices
<br>●    Artifacts removal from mobile photos/videos
<br>●    General smartphone photo/video enhancement
<br>●    RAW camera image/video processing
<br>●    Deep learning applications for mobile camera ISPs
<br>●    Image/video super-resolution on low-power hardware
<br>●    Portrait segmentation / bokeh effect rendering
<br>●    Depth estimation w/o multiple cameras
<br>●    Perceptual image manipulation on mobile devices
<br>●    Activity recognition using smartphone sensors
<br>●    Image/sensor based identity recognition
<br>●    Fast image classification / object detection algorithms
<br>●    NLP models optimized for mobile inference
<br>●    Real-time semantic segmentation
<br>●    Low-power machine learning inference
<br>●    Machine learning and deep learning frameworks for mobile devices
<br>●    AI performance evaluation / benchmarking of mobile and IoT hardware
<br>●    Studies and applications of the above problems
<br></div>
<br>


</div></div><span><span><span><span><span><span><span><span><span><span><span></span></span></span></span></span></span></span></span></span></span></span>

</div><div>SUBMISSION</div><div><br></div><div>
<div>A <span><span>paper</span></span> submission has to be in English, in pdf format, and at most 8
 pages (excluding references) in CVPR style. <br></div>
<a href="http://cvpr2021.thecvf.com/node/33" target="_blank">http://cvpr2021.thecvf.com/node/33</a>

<div>The review process is double blind. <br>
</div><div>Accepted and presented <span><span>papers</span></span> will be published
 in the CVPR 2021 Workshops Proceedings.
<br>
<br>Author Kit: 
<a href="http://cvpr2021.thecvf.com/sites/default/files/2020-09/cvpr2021AuthorKit_2.zip" target="_blank">http://cvpr2021.thecvf.com/sites/default/files/2020-09/cvpr2021AuthorKit_2.zip</a>

</div><div>Submission site: 
<a href="https://cmt3.research.microsoft.com/MAI2021">https://cmt3.research.microsoft.com/MAI2021</a>


</div></div></div><div><br></div><div>WORKSHOP DATES</div><div><br></div><div>
<div><div style="margin-left:40px">
● <b>Regular <span>Papers</span> Submission Deadline: March 05, <span><span>2021</span></span></b><span><span> </span></span><b><span><span><br></span></span></b></div><div style="margin-left:40px"><span><span></span></span></div><div style="margin-left:40px">● Challenge <span>Papers</span> Submission Deadline: March 28, <span><span>2021</span></span></div><div style="margin-left:40px"><span><span><br></span></span></div>
<div><div>CHALLENGES</div><div style="margin-left:40px"><br></div><div style="margin-left:40px">
●   <b>Learned Smartphone ISP</b> (Evaluation platform: MediaTek Dimensity APU) - Powered by MediaTek
<br>     ●   <b>Image Denoising</b> (Eval. platform: Exynos Mali GPU) - Powered by Samsung
<br>     ●   <b>HDR Image Processing</b> (Eval. platform: Kirin DaVinci NPU) - Powered by Huawei
<br>     ●   <b>Image Super-Resolution</b> (Eval. platform: Synaptics Dolphin NPU) - Powered by Synaptics
<br>     ●   <b>Video Super-Resolution</b> (Eval. platform: Snapdragon Adreno GPU) - Powered by OPPO
<br>     ●   <b>Depth Estimation</b> (Eval. platform: Raspberry Pi 4) - Powered by Raspberry Pi
<br>     ● <b>  Camera Scene Detection</b> (Eval. platform: Apple Bionic) - Powered by CVL
<br>


</div></div><div><br></div><div>To learn more about the challenges, to participate <span>in</span> the challenges, 
<span>and</span> to access the data everybody is invited to check the <span><span>Mobile AI</span></span> 2021 web page:</div><div>
<a href="https://ai-benchmark.com/workshops/mai/2021/">https://ai-benchmark.com/workshops/mai/2021/</a>

<div><div><br></div><div>For those interested in restoration, enhancement, manipulation, classification without specific mobile hardware constraints we refer to the CVPR21<b> NTIRE Workshop and Challenges:</b></div><div><b>
</b><a href="https://data.vision.ee.ethz.ch/cvl/ntire21/">https://data.vision.ee.ethz.ch/cvl/ntire21/</a></div><div><br></div><div>
CHALLENGES DATES<br><div>
<br><div style="margin-left:40px">● Release of train data: January 10, <span><span><span>2020</span></span></span><br>● <b>Competitions end: March 08, <span><span><span>2020</span></span></span></b><span><span><span><span><span></span></span><span><span></span></span></span></span></span><b><span><span><span><b><span><span><br></span></span></b></span></span></span></b></div><div style="margin-left:40px"><b><span><span><span><br></span></span></span></b></div><b><span><span><span>
</span></span></span></b><span><span><span>ORGANIZERS (TBU)<br>
<br></span></span></span><div style="margin-left:40px">
●    Andrey Ignatov ( ETH Zurich)
<br>    ●    Radu Timofte ( ETH Zurich)
<br>    ●    Luc Van Gool ( ETH Zurich and KU Leuven)
<br>    ●    Martti Ilmoniemi ( Huawei Technologies Oy (Finland) Co. Ltd)
<br>    ●    Cheng-Ming Chiang ( MediaTek Inc.) 
<br>    ●    Hsien-Kai Kuo ( MediaTek) 
<br>    ●    Kim Byeoung-su ( Samsung Electronics Co., Ltd.)
<br>    ●    Gaurav Arora ( Synaptics Inc.)
<br>    ●    Abdel Younes ( Synaptics Inc.)
<br>    ●    David Plowman ( Raspberry Pi (Trading) Ltd.)
<br>    ● Heewon Kim ( Seoul National University)
<br>    ●    Kyoung Mu Lee ( Seoul National University)
<br>    ●    Eirikur Agustsson ( Google)
<br>    ●    Chiu Man Ho ( OPPO)
<br>    ●    Zibo Meng ( OPPO)
<br>    ●    Shuhang Gu ( University of Sydney, OPPO)<br><span><span><span></span></span></span></div><div style="margin-left:40px"><br><span><span><span></span></span></span><b><span><span><span></span></span></span></b></div>
</div><div>
SPEAKERS (TBA) <br></div><div><br></div><div>SPONSORS (TBU)</div><div><br></div><div>
● OPPO</div><div>    ●    Samsung<br>    </div><div>
<div>
● MediaTek

</div><div>● Synaptics<br></div><div>    ● Huawei<br>    </div>

</div><div>
<div></div><div>    ● Raspberry Pi<br>    </div>

</div><div>
● CVL / ETH Zurich

</div></div><div><br></div><div><br><div>Website: 
<a href="https://ai-benchmark.com/workshops/mai/2021/">https://ai-benchmark.com/workshops/mai/2021/</a>



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