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<!DOCTYPE html><html lang="en-us" >
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<a class="nav-link " href="/#about" data-target="#about"><span>About me</span></a>
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<a class="nav-link " href="/#publications" data-target="#publications"><span>Publication</span></a>
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<div class="portrait-title">
<h2>Ruihao Gong</h2>
<h3>Graduate Student</h3>
<h3>
<span>Beihang University</span>
</h3>
</div>
<ul class="network-icon" aria-hidden="true">
<li>
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<a href="https://www.zhihu.com/people/xiao-hao-zi-46" target="_blank" rel="noopener">
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<p>Ruihao Gong is currently a senior researcher at SenseTime. Before this, he studied at Beihang University under the supervision of Prof.
<a href="http://sites.nlsde.buaa.edu.cn/~xlliu/" target="_blank" rel="noopener">Xianglong Liu</a>. Since 2017, he worked on the build-up of computer vision systems and model quantization as an intern at Sensetime Research, where he enjoyed working with the talented researchers and grew up a lot with the help of
<a href="http://forwil.xyz/" target="_blank" rel="noopener">Fengwei Yu</a>,
<a href="https://wuwei-ai.org/" target="_blank" rel="noopener">Wei Wu</a>,
<a href="https://amandajshao.github.io/" target="_blank" rel="noopener">Jing Shao</a>, and
<a href="https://yan-junjie.github.io/" target="_blank" rel="noopener">Junjie Yan</a>. During the early time of the internship, he independently took responsibility for the development of intelligent video analysis system Sensevideo. Later, he started the research on model quantization which can speed up the inference and even the training of neural networks on edge devices. Now he is devoted to further promoting the accuracy of extremely low-bit models and the auto-deployment of quantized models.</p>
<p><em><font color=Red>We are hiring!</font></em> Positions are available for self-motivated interns and full-time researchers/engineers in AI and System. Please do not be hesitated to send your CV to gongruihao [at] sensetime.com!</p>
<div class="row">
<div class="col-md-5">
<h3>Interests</h3>
<ul class="ul-interests">
<li>Model Quantization</li>
<li>Computer Vision</li>
<li>CNN Compiler</li>
<li>System on Machine Learning</li>
</ul>
</div>
<div class="col-md-7">
<h3>Education</h3>
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<li>
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<div class="description">
<p class="course">MEng in Computer Sicence, 2018.9 - 2021.1</p>
<p class="institution">Beihang University</p>
</div>
</li>
<li>
<i class="fa-li fas fa-graduation-cap"></i>
<div class="description">
<p class="course">BSc in Computer Science, 2014.9 - 2018.6</p>
<p class="institution">Beihang University</p>
</div>
</li>
</ul>
</div>
</div>
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</section>
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<h1>Publications</h1>
</div>
<div class="col-12 col-lg-12">
<div class="media stream-item">
<div class="media-body">
<h3 class="article-title mb-0 mt-0">
<a href="https://openreview.net/pdf?id=POWv6hDd9XH" target="_blank" rel="noopener">BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction</a>
</h3>
<div class="stream-meta article-metadata">
<div>
Yuhang Li*, <b>Ruihao Gong*</b>, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, Shi Gu
</div>
ICLR 2021.
<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="/publication/conference-paper/iclr2021/brecq/BRECQ.pdf" target="_blank" rel="noopener">
PDF
</a>
<button type="button" class="btn btn-outline-primary my-1 mr-1 btn-sm js-cite-modal"
data-filename="/publication/conference-paper/iclr2021/brecq/cite.bib">
Cite
</button>
<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/yhhhli/BRECQ" target="_blank" rel="noopener">
Code
</a>
</div>
</div>
<div class="ml-3">
<a href="https://openreview.net/pdf?id=POWv6hDd9XH" target="_blank" rel="noopener">
<img src="/publication/conference-paper/iclr2021/brecq/featured_hua5d4373f708573fe221e70e32a73e377_71403_350x0_resize_lanczos_2.png" alt="">
</a>
</div>
</div>
<div class="media stream-item">
<div class="media-body">
<h3 class="article-title mb-0 mt-0">
<a href="http://openaccess.thecvf.com/content_ICCV_2019/html/Gong_Differentiable_Soft_Quantization_Bridging_Full-Precision_and_Low-Bit_Neural_Networks_ICCV_2019_paper.html" target="_blank" rel="noopener">Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks</a>
</h3>
<div class="stream-meta article-metadata">
<div>
<b>Ruihao Gong</b>, Xianglong Liu*, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, Junjie Yan
</div>
IEEE ICCV 2019.
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PDF
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Cite
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Poster
</a>
</div>
</div>
<div class="ml-3">