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<!DOCTYPE HTML>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Kun-Yu Lin</title>
<meta name="author" content="Kun-Yu Lin">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="shortcut icon" href="images/favicon/favicon.ico" type="image/x-icon">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;vertical-align:middle"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p class="name" style="text-align: center;">
Kun-Yu Lin
</p>
<p style="font-size:16px;">
I am now a post-doctoral research fellow at the University of Hong Kong, under the supervision of <a href="https://www.kaihan.org/">Prof. Kai Han</a>.
I obtained my PhD degree from Sun Yat-set University, under the supervision of <a href="https://www.isee-ai.cn/~zhwshi/index.html">Prof. Wei-Shi Zheng</a>.
Prior to this, I obtained my Bachelor's degree and Master degree from Sun Yat-Sen University.
During my PhD, I was fortunate to have the opportunity to study as a visiting student at MMLab@NTU, under the supervision of <a href="https://www.mmlab-ntu.com/person/ccloy/">Prof. Chen Change Loy</a> and <a href="https://henghuiding.github.io/">Prof. Henghui Ding</a>.
My research interests include computer vision and machine learning.
</p>
<p style="text-align:center">
<a href="mailto:kunyulin14@outlook.com">Email</a> /
<a href="https://scholar.google.com/citations?hl=en&user=tkUBeeQAAAAJ">Scholar</a> /
<a href="https://github.com/kunyulin/">Github</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/JonBarron.jpg"><img style="width:80%;max-width:80%;object-fit: cover; border-radius: 50%;" alt="profile photo" src="images/kunyu.jpg" class="hoverZoomLink"></a>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<h2>News</h2>
</td>
</tr>
<tr style="font-size:15px;"> <td> <b>❅ 12/2024:</b> One paper was accepted to AAAI2025.
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 07/2024:</b> One paper was accepted to TPAMI.
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 03/2024:</b> Releasing <a href="https://arxiv.org/abs/2403.01560">XOV-Action</a>, the first cross-domain open-vocabulary action recognition benchmark!
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 09/2023:</b> One paper was accepted to NeurIPS2023.
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 09/2023:</b> One paper was accepted to TPAMI.
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 07/2023:</b> One paper was accepted to ICCV2023.
</td></tr>
<tr style="font-size:15px;"> <td> <b>❅ 03/2023:</b> Two papers were accepted to CVPR2023.
</td></tr>
<tr> <td> </td></tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<h2>Selected Works</h2>
<p style="font-size:15px;">
Most of my research works are about human video understanding, transferable, generalizable and trustworthy deep learning, and vision-language models.
Some works are <span class="highlight">highlighted</span>.
# denotes equal contributions.
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr onmouseout="xov_action()" onmouseover="xov_action()" bgcolor="#ffffd0">
<!-- <tr onmouseout="nuvo_stop()" onmouseover="nuvo_start()"> -->
<td style="padding:20px;width:25%;vertical-align:middle">
<!-- <div class="one"> -->
<!-- <div class="two" id='xovaction'><video width=100% muted autoplay loop>
<source src="images/nuvo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div> -->
<img src='images/xovaction.png' width=100%>
<!-- </div> -->
<!-- <script type="text/javascript">
function nuvo_start() {
document.getElementById('nuvo_image').style.opacity = "1";
}
function nuvo_stop() {
document.getElementById('nuvo_image').style.opacity = "0";
}
nuvo_stop()
</script> -->
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2403.01560">
<span class="papertitle">Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition</span>
</a>
<br>
<strong>Kun-Yu Lin</strong>, Henghui Ding, Jiaming Zhou, Yu-Ming Tang, Yi-Xing Peng, Zhilin Zhao, Chen Change Loy, Wei-Shi Zheng
<!-- <a href="https://bmild.github.io/">Ben Mildenhall</a> -->
<br>
<em>arXiv</em>, 2024
<br>
<a href="https://arxiv.org/abs/2403.01560">arXiv</a>
/
<a href="https://github.com/KunyuLin/XOV-Action/">github</a>
<p></p>
<p>
The first benchmark, named <a href="https://github.com/KunyuLin/XOV-Action/">XOV-Action</a>, for the cross-domain open-vocabulary action recognition task,
and a simple yet effective method to address the scene bias for the task.
</p>
</td>
</tr>
<tr onmouseout="pargo()" onmouseover="pargo()" bgcolor="#ffffd0">
<!-- <tr onmouseout="nuvo_stop()" onmouseover="nuvo_start()"> -->
<td style="padding:20px;width:25%;vertical-align:middle">
<!-- <div class="one"> -->
<!-- <div class="two" id='pargo'><video width=100% muted autoplay loop>
<source src="images/nuvo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div> -->
<img src='images/pargo.png' width=100%>
<!-- </div> -->
<!-- <script type="text/javascript">
function nuvo_start() {
document.getElementById('nuvo_image').style.opacity = "1";
}
function nuvo_stop() {
document.getElementById('nuvo_image').style.opacity = "0";
}
nuvo_stop()
</script> -->
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2408.12928">
<span class="papertitle">ParGo: Bridging Vision-Language with Partial and Global Views</span>
</a>
<br>
An-Lan Wang, Bin Shan, Wei Shi, <strong>Kun-Yu Lin</strong>, Xiang Fei, Guozhi Tang, Lei Liao, Jingqun Tang, Can Huang, Wei-Shi Zheng
<!-- <a href="https://bmild.github.io/">Ben Mildenhall</a> -->
<br>
<em>AAAI</em>, 2025
<br>
<a href="https://arxiv.org/abs/2408.12928">arXiv</a>
<p></p>
<p>
A novel connector for bridging vision and language modalities by both global and partial views, and a large-scale image-text datasets consisting of detailed captions.
</p>
</td>
</tr>
<tr onmouseout="huamnrobotalign()" onmouseover="humanrobotalign()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/humanrobotalign.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2406.14235">
<span class="papertitle">Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation</span>
</a>
<br>
Jiaming Zhou, Teli Ma, <strong>Kun-Yu Lin</strong>, Ronghe Qiu, Zifan Wang, Junwei Liang
<br>
<em>arXiv</em>, 2024
<br>
<a href="https://arxiv.org/abs/2406.14235">arXiv</a>
/
<a href="https://jiaming-zhou.github.io/projects/HumanRobotAlign/">project page</a>
<p></p>
<p>
A new paradigm utilizing paired human-robot videos to adapt human-data pretrained models for robotic manipulation tasks.
</p>
</td>
</tr>
<tr onmouseout="hctransformer()" onmouseover="hctransformer()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/hctransformer.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10599825">
<span class="papertitle">Human-Centric Transformer for Domain Adaptive Action Recognition</span>
</a>
<br>
<strong>Kun-Yu Lin</strong>, Jiaming Zhou, Wei-Shi Zheng
<br>
<em>TPAMI</em>, 2024
<br>
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10599825">paper</a>
/
<a href="https://arxiv.org/abs/2407.10860">arXiv</a>
<p></p>
<p>
A human-centric video network to address the context bias in domain adaptive action recognition.
</p>
</td>
</tr>
<tr onmouseout="stdn()" onmouseover="stdn()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/stdn.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2310.17942">
<span class="papertitle">Diversifying Spatial-Temporal Perception for Video Domain Generalization</span>
</a>
<br>
<strong>Kun-Yu Lin</strong>, Jia-Run Du, Yipeng Gao, Jiaming Zhou, Wei-Shi Zheng
<br>
<em>NeurIPS</em>, 2023
<br>
<a href="https://openreview.net/forum?id=YsZTDcIQwQ">paper</a>
/
<a href="https://arxiv.org/abs/2310.17942">arXiv</a>
/
<a href="https://github.com/KunyuLin/STDN/">github</a>
<p></p>
<p>
A diversity-aware video network to address the bias to domain-specific information in video domain generalization.
</p>
</td>
</tr>
<tr onmouseout="e3p()" onmouseover="e3p()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/e3p.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_Event-Guided_Procedure_Planning_from_Instructional_Videos_with_Text_Supervision_ICCV_2023_paper.pdf">
<span class="papertitle">Event-Guided Procedure Planning from Instructional Videos with Text Supervision</span>
</a>
<br>
An-Lan Wang#, <strong>Kun-Yu Lin#</strong>, Jia-Run Du, Jingke Meng, Wei-Shi Zheng
<br>
<em>ICCV</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_Event-Guided_Procedure_Planning_from_Instructional_Videos_with_Text_Supervision_ICCV_2023_paper.pdf">paper</a>
/
<a href="https://arxiv.org/abs/2308.08885">arXiv</a>
<p></p>
<p>
A new event-guided paradigm to address the semantic gap between observed states and unobserved actions for procedure planning in instructional videos.
</p>
</td>
</tr>
<tr onmouseout="asyfod()" onmouseover="asyfod()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/asyfod.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_AsyFOD_An_Asymmetric_Adaptation_Paradigm_for_Few-Shot_Domain_Adaptive_Object_CVPR_2023_paper.pdf">
<span class="papertitle">AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection</span>
</a>
<br>
Yipeng Gao#, <strong>Kun-Yu Lin#</strong>, Junkai Yan, Yaowei Wang, Wei-Shi Zheng
<br>
<em>CVPR</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_AsyFOD_An_Asymmetric_Adaptation_Paradigm_for_Few-Shot_Domain_Adaptive_Object_CVPR_2023_paper.pdf">paper</a>
/
<a href="https://github.com/Hlings/AsyFOD">github</a>
<p></p>
<p>
An asymmetric adaptation paradigm for few-shot domain adaptive object detection.
</p>
</td>
</tr>
<tr onmouseout="stdn()" onmouseover="stdn()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/dilateformer.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2302.01791">
<span class="papertitle">DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition</span>
</a>
<br>
Jiayu Jiao#, Yu-Ming Tang#, <strong>Kun-Yu Lin</strong>, Yipeng Gao, Jinhua Ma, Yaowei Wang, Wei-Shi Zheng
<br>
<em>TMM</em>, 2023
<br>
<a href="https://ieeexplore.ieee.org/abstract/document/10041780/">paper</a>
/
<a href="https://arxiv.org/abs/2302.01791">arXiv</a>
/
<a href="https://isee-ai.cn/~jiaojiayu/DilteFormer.html">project page</a>
/
<a href="https://github.com/JIAOJIAYUASD/dilateformer">github</a>
<p></p>
<p>
A new vision transformer architecture for efficient and effective visual understanding.
</p>
</td>
</tr>
<tr onmouseout="ood_sa()" onmouseover="ood_sa()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/ood_sa.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2206.09380">
<span class="papertitle">Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection</span>
</a>
<br>
Zhilin Zhao, Longbing Cao, <strong>Kun-Yu Lin</strong>
<br>
<em>TPAMI</em>, 2023
<br>
<a href="https://ieeexplore.ieee.org/document/10271740/">paper</a>
/
<a href="https://arxiv.org/abs/2206.09380">arxiv</a>
/
<a href="https://github.com/Lawliet-zzl/SA">github</a>
<p></p>
<p>
A theorectical method to balancing in-distribution generalization and out-of-distribution detection.
</p>
</td>
</tr>
<tr onmouseout="ood_fig()" onmouseover="ood_fig()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/ood_fig.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2108.09976">
<span class="papertitle">Revealing the Distributional Vulnerability of Discriminators by Implicit Generators</span>
</a>
<br>
Zhilin Zhao, Longbing Cao, <strong>Kun-Yu Lin</strong>
<br>
<em>TPAMI</em>, 2023
<br>
<a href="https://ieeexplore.ieee.org/document/9987694/">paper</a>
/
<a href="https://arxiv.org/abs/2108.09976">arxiv</a>
/
<a href="https://github.com/Lawliet-zzl/FIG">github</a>
<p></p>
<p>
A theorectical method based on implicit generators to improve out-of-distribution detection.
</p>
</td>
</tr>
<tr onmouseout="cwan()" onmouseover="cwan()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/cwan.png' width=100%>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136930520.pdf">
<span class="papertitle">Adversarial Partial Domain Adaptation by Cycle Inconsistency</span>
</a>
<br>
<strong>Kun-Yu Lin</strong>, Jiaming Zhou, Yukun Qiu, Wei-Shi Zheng
<br>
<em>ECCV</em>, 2022
<br>
<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136930520.pdf">paper</a>
/
<a href="https://github.com/KunyuLin/CWAN">github</a>
<p></p>
<p>
A simple yet effective method based on cycle transformation to filter out outlier classes in partial domain adaptation.
</p>
</td>
</tr>
</tbody></table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20"><tbody>
<tr>
<td>
<h2>Services</h2>
</td>
</tr>
</tbody></table>
<table width="100%" align="center" border="0" cellpadding="20"><tbody>
<tr>
<!-- <td style="padding:20px;width:25%;vertical-align:middle"><img src="images/cvf.jpg"></td> -->
<td style="padding:20px;width:25%;vertical-align:middle"><img src="images/services.png" width=100%></td>
<td width="75%" valign="center">
Reviewer of CVPR23, CVPR24
<br>
Reviewer of ICCV23
<br>
Reviewer of ECCV24
<br>
Reviewer of ICLR25
<br>
Reviewer of NeurIPS24
<br>
Reviewer of IJCAI24
<br>
Reviewer of TCSVT
<br>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:0px">
<br>
<p style="text-align:right;font-size:small;">
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