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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<title>Yanjing Li</title>
<!--<link rel="stylesheet" type="text/css" href="/imgs/css" >-->
<link rel="icon" type="image/jpg" href="https://YanjingLi0202.github.io/imgs/buaa_icon.png">
</head>
<body>
<table width="800" border="0" align="center" cellspacing="0" cellpadding="0">
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<tr>
<td>
<!--SECTION 1 -->
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
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<td width="75%" valign="middle">
<p align="center"><name>Yanjing Li</name></p>
<p align="justify">I am a PhD candidate (2020.09-) at the School of Electronics and Information Engineering,
<a href="https://ev.buaa.edu.cn/">Beihang University</a>,
supervised by Prof. <a href="https://shi.buaa.edu.cn/caoxianbin/en/index.htm">Xianbin Cao</a>
and Prof. <a href="https://scholar.google.com/citations?user=ImJz6MsAAAAJ">Baochang Zhang</a>.
I obtained my BSc degree in Shen Yuan Honors College (Electronics and Information Engineering) from <a href="https://ev.buaa.edu.cn/">Beihang University</a> (2016.09-2020.06).
<!-- <br><br> -->
Now I am a research intern (2024.05-) at TikTok of <a href="https://www.bytedance.com/">ByteDance</a>, and I was interned at <a href="https://www.shlab.org.cn">Shanghai AI Lab</a> and <a href="https://www.sensetime.com/en">Fundamental Vision Group of Sensetime</a>.
<br><br>
<strong>Email:</strong> [email protected]
<br>
</p><p align="center">
<a href="https://scholar.google.com/citations?user=2rE-GM8AAAAJ">Google Scholar</a> /
<a href="https://github.com/YanjingLi0202/"> Github </a> /
<a href="https://www.linkedin.com/in/yanjing-li-b3105a179"> Linkedin </a> /
<a href="imgs/Yanjing_Li_BUAA_CV.pdf"> CV(中文) </a> /
<a href="imgs/CV_EN.pdf"> CV(EN) </a>
</p>
</td>
<td align="right"> <img class="hp-photo" src="./imgs/yanjingli_img.png" style="width: 180;">
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<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
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<td>
<heading>Research</heading>
<p align="justify">My area of interest lies in the techniques of <strong><i>network binarization and quantization</i></strong>, along with knowledge distillation.
My research objective is to facilitate the deployment of advanced neural network models on hardware with limited resources.
This involves compressing various neural architectures and ensuring their adaptable deployment on diverse hardware platforms. My research focus is mainly on:
<li>Network binarization and quantization</li>
<li>Knowledge distillation</li>
<li>Image synthesizing</li>
<li>Object detection</li>
</td></tr>
</tbody>
</table>
<!--SECTION 3 -->
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tbody>
<td>
<heading>Selected Publications<a name="publications"></a>
</heading>
<p>
Full list can be found on <a href="https://scholar.google.com/citations?user=2rE-GM8AAAAJ">Google Scholar</a>.
</p>
</td></tbody>
</table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tbody>
<tr><td width="20%"><img src="./imgs/q-imaging.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Learning Accurate Low-bit Quantization towards Efficient Computational Imaging</papertitle></a>
<br>Sheng Xu<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Chuanjian Liu, Baochang Zhang
<br>
<em>International Journal of Computer Vision (IJCV)</em>, 2024
<br>
<a href="#"><font color="black">[Paper coming]</font></a> /
<a href="#"><font color="black">[arXiv coming]</font></a> /
<a href="#"><font color="black">[code coming]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/dfr.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement</papertitle></a>
<br>Sheng Xu<sup>*</sup>, Mingze Wang<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Mingbao Lin, Baochang Zhang, David Doermann, Xiao Sun
<br>
<em>International Conference on Machine Learning (ICML)</em>, 2024
<br>
<a href="#"><font color="black">[Paper coming]</font></a> /
<a href="#"><font color="black">[arXiv coming]</font></a> /
<a href="#"><font color="black">[code coming]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/bi_vit.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Bi-ViT: Pushing the Limit of Vision Transformer Quantization</papertitle></a>
<br><strong>Yanjing Li</strong><sup>*</sup>, Sheng Xu<sup>*</sup>, Mingbao Lin, Xianbin Cao, Chuanjian Liu, Xiao Sun, Baochang Zhang
<br>
<em>AAAI Conference on Artificial Intelligence (AAAI)</em>, 2024
<br>
<a href="#"><font color="black">[Paper coming]</font></a> /
<a href="https://arxiv.org/abs/2305.12354"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/YanjingLi0202/Bi-ViT/"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/q-dm.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Q-DM: An Efficient Low-bit Quantized Diffusion Model</papertitle></a>
<br><strong>Yanjing Li</strong><sup>*</sup>, Sheng Xu<sup>*</sup>, Xianbin Cao, Xiao Sun, Baochang Zhang
<br>
<em> Conference on Neural Information Processing Systems (NeurIPS)</em>, 2023
<br>
<a href="https://openreview.net/pdf?id=sFGkL5BsPi"><font color="goldenrod">[Paper]</font></a> /
<a href="#"><font color="black">[arXiv coming]</font></a> /
<a href="#"><font color="black">[code coming]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/rdd.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Representation Disparity-aware Distillation for 3D Object Detection</papertitle></a>
<br><strong>Yanjing Li</strong><sup>*</sup>, Sheng Xu<sup>*</sup>, Mingbao Lin, Jihao Yin, Baochang Zhang, Xianbin Cao
<br>
<em>International Conference on Computer Vision (ICCV)</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Li_Representation_Disparity-aware_Distillation_for_3D_Object_Detection_ICCV_2023_paper.pdf"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/abs/2308.10308"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/YanjingLi0202/RDD"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/dcp_nas.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs</papertitle></a>
<br><strong>Yanjing Li</strong><sup>*</sup>, Sheng Xu<sup>*</sup>, Xianbin Cao, Li'an Zhuo, Baochang Zhang, Tian Wang, Guodong Guo
<br>
<em>International Journal of Computer Vision (IJCV)</em>, 2023
<br>
<a href="https://link.springer.com/article/10.1007/s11263-023-01836-4"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/abs/2306.15390"><font color="goldenrod">[arXiv]</font></a> /
<a href="#"><font color="black">[code coming]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/q-detr.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Q-DETR: An Efficient Low-Bit Quantized Detection Transformer</papertitle></a>
<br>Sheng Xu<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Mingbao Lin, Peng Gao, Guodong Guo, Jinhu Lu, Baochang Zhang
<br>
<em>Computer Vision and Pattern Recognition (CVPR)</em>, 2023
<em><font color="red">Highlight presentation</font></em>
<br>
<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Q-DETR_An_Efficient_Low-Bit_Quantized_Detection_Transformer_CVPR_2023_paper.pdf"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/abs/2304.00253"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/SteveTsui/Q-DETR"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/idm.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Implicit Diffusion Models for Continuous Super-Resolution</papertitle></a>
<br>Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, <strong>Yanjing Li</strong>, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang
<br>
<em>Computer Vision and Pattern Recognition (CVPR)</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.pdf"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/abs/2303.16491"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/Ree1s/IDM"><font color="goldenrod">[code]</font></a>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=#&repo=#&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/rebnn.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="#">
<papertitle>Resilient Binary Neural Network</papertitle></a>
<br>Sheng Xu<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Teli Ma<sup>*</sup>, Mingbao Lin, Hao Dong, Baochang Zhang, Peng Gao, Jinhu Lu
<br>
<em>AAAI Conference on Artificial Intelligence (AAAI)</em>, 2023
<em><font color="red">Oral presentation</font></em>
<br>
<a href="#"><font color="black">[Paper coming]</font></a> /
<a href="https://arxiv.org/abs/2302.00956"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/SteveTsui/ReBNN"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=SteveTsui&repo=ReBNN&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p><p></p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/q-vit.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="https://openreview.net/pdf?id=fU-m9kQe0ke">
<papertitle>Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer</papertitle></a>
<br><strong>Yanjing Li</strong><sup>*</sup>, Sheng Xu<sup>*</sup>, Baochang Zhang, Xianbin Cao, Peng Gao, Guodong Guo
<br>
<em> Conference on Neural Information Processing Systems (NeurIPS)</em>, 2022
<br>
<a href="https://openreview.net/pdf?id=fU-m9kQe0ke"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/pdf/2210.06707.pdf"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/YanjingLi0202/Q-ViT"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=YanjingLi0202&repo=Q-ViT&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p><p></p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/rbonn.png" alt="PontTuset" width="180" style="border-style: none"></td>
<td width="80%" valign="top">
<p><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136840019.pdf">
<papertitle>Recurrent Bilinear Optimization for Binary Neural Networks</papertitle></a>
<br>Sheng Xu<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Tiancheng Wang, Teli Ma, Baochang Zhang, Peng Gao, Yu Qiao, Jinhu Lv, Guodong Guo
<br>
<em>European Conference on Computer Vision (ECCV)</em>, 2022
<em><font color="red">Oral presentation</font></em>
<br>
<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136840019.pdf"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/pdf/2209.01542.pdf"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/SteveTsui/RBONN"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
<!-- <iframe src="https://ghbtns.com/github-btn.html?user=SteveTsui&repo=RBONN&type=star&count=true&size=small"
frameborder="0" scrolling="0" width="100px" height="20px"></iframe> -->
</p><p></p>
<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
<!-- <p></p> -->
<hr />
</td>
</tr>
<tr><td width="20%"><img src="./imgs/ida-det.png" alt="PontTuset" width="180" style="border-style: none"></td>
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<p><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136710347.pdf">
<papertitle>IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors</papertitle></a>
<br>Sheng Xu<sup>*</sup>, <strong>Yanjing Li</strong><sup>*</sup>, Bohan Zeng<sup>*</sup>, Baochang Zhang, Xianbin Cao, Peng Gao, Jinhu Lv
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<em>European Conference on Computer Vision (ECCV)</em>, 2022
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<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136710347.pdf"><font color="goldenrod">[Paper]</font></a> /
<a href="https://arxiv.org/pdf/2210.03477.pdf"><font color="goldenrod">[arXiv]</font></a> /
<a href="https://github.com/SteveTsui/IDa-Det"><font color="goldenrod">[code]</font></a> <p align="justify" style="font-size:10px">(<sup>*</sup> Equal Contribution)</p>
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<!-- <p align="justify" style="font-size:13px">In this paper, we propose Q-DETR.</p> -->
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<td><heading>Academic Services</heading>
<p> <strong>Program Committee</strong> of Conferences: CVPR 2022/2023, ECCV 2022, ICCV 2023, NeurIPS 2023, ICLR 2024, etc.</p>
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