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<!DOCTYPE HTML>
<html lang="en">
<head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<link href="stylesheet.css" rel="stylesheet" type="text/css">
<link rel="icon" type="image/x-icon" href="images/cornell_icon.png">
<title>Noriyuki Kojima・コジマ ノリユキ・小島 熙之</title>
<meta http-equiv="Content-Type" content="text/html; charset=us-ascii">
<link href='https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><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 style="text-align:center">
<name>Noriyuki Kojima</name>
</p>
<p> I am a Co-Founder and CEO of <a href="https://www.kotoba.tech">Kotoba Technologies, Inc</a>.
</p>
<p>
Previously, I graduated from CS Ph.D at Cornell University in 2023, where I was advised by <a href="https://yoavartzi.com/">Yoav Artzi</a>. I am grateful that My Ph.D. study was supported by Cornell Hopcroft Fellowship (2019-2020) and <a href="https://masason-foundation.org/en/">Masason Fellowship</a> (2020~2022).
I was a Research Scientist Intern at Hugging Face in summer 2022, working with <a href="https://rush-nlp.com/h">Sasha Rush</a>. and <a href="https://huggingface.co/VictorSanh">Victor Sanh</a>;
I was a Research Specialist at Princeton University in 2019, working with <a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>; I did my undergraduate at the University of Michigan from 2015 to 2018 with B.S.E in Computer Science where I worked with <a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>, <a href="https://web.eecs.umich.edu/~mihalcea/">Rada Mihalcea</a> and <a href="https://cpsc.yale.edu/people/dragomir-radev">Dragomir Radev</a>; I was a Software Engineer Intern in the Facebook Applied Machine Learning team in summer 2017, where I was supervised by <a href="https://research.fb.com/people/pino-juan/">Juan Pino</a>.
</p>
<p style="text-align:center">
<a href="[email protected]">Email</a>  / 
<!-- <a href="data/JonBarron-CV.pdf">CV</a>  / -->
<!-- <a href="data/JonBarron-bio.txt">Biography</a>  / -->
<a href="https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AH70aAVUOC4bbDuodP9Rh5ZaloPH36HTSjmxFj-u_cvNVfejnkZwU9nZFZ9ZFoDNDP780MoApY0nIe5LmADA5w&user=ycAbN_oAAAAJ">Google Scholar</a>  / 
<a href="https://www.linkedin.com/in/kojimano/"> LinkedIn </a>  / 
<a href="https://twitter.com/noriyuki_kojima"> Twitter </a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/profile-2023-2-26.jpg"><img style="width:100%;max-width:100%" alt="profile photo" src="images/profile-2023-2-26.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">
<heading>Research</heading>
<p>
I'm interested in natural language processing, computer vision, and machine learning. One of the core topics in my research is about how to learn grounded language systems thorugh interactions with human users. Representative papers are <span class="highlight">highlighted</span>.
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<!-- A joint study of phrase grounding and task performance in vision and language models -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/phrase_grounding.png" type="video/mp4" style=padding-top:40px;>
</video></div>
<img src='images/phrase_grounding.png' width="160" style=padding-top:40px;>
</div>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/pdf/2309.02691.pdf">
<papertitle>A joint study of phrase grounding and task performance in vision and language models </papertitle>
</a>
<br>
<strong>Noriyuki Kojima</strong>,
<a href=http://www.cs.cornell.edu/~hadarelor/>Hadar Averbuch-Elor</a>,
<a href="https://yoavartzi.com/">Yoav Artzi</a>
<br>
<em>TMLR</em>, 2024
<br>
<a href="https://arxiv.org/pdf/2309.02691.pdf">arXiv</a> /
<a href="https://github.com/lil-lab/phrase_grounding">code</a>
<p> Presenting a framework to jointly study vision and language tasks and the fundamental steps of phrase grounding. </p>
</td>
</tr>
<!-- Abstract Visual Reasoning with Tangram Shapes -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/kilogram.png" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='images/kilogram.png' width="160" style=padding-top:20px;>
</div>
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<a href="https://arxiv.org/pdf/2211.16492.pdf">
<papertitle>Abstract Visual Reasoning with Tangram Shapes </papertitle>
</a>
<br>
<a href="https://anya-ji.github.io/">Anya Ji</a>,
<strong>Noriyuki Kojima</strong>,
Noah Rush,
<a href="https://www.alanesuhr.com/">Alane Suhr</a>,
<a href="https://www.waikeenvong.com//">Wai Keen Vong</a>,
<a href="https://rxdhawkins.com/">Robert D. Hawkins</a>,
<a href="https://yoavartzi.com/">Yoav Artzi</a>
<br>
<em>EMNLP</em>, 2022 <font color="red"><strong>(Best Paper Award)</strong></font>
<br>
<a href="https://arxiv.org/pdf/2211.16492.pdf">arXiv</a> /
<a href="https://github.com/lil-lab/kilogram">code</a>
<p> Presenting KILOGRAM, a resource for
studying abstract visual reasoning in humans
and machines.</p>
</td>
</tr>
<!-- lilGym: Natural Language Visual Reasoning with Reinforcement Learning -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="videos/lilgym_gold_scatter_flipit_ex.gif" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='videos/lilgym_gold_scatter_flipit_ex.gif' width="160" style=padding-top:20px;>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/pdf/2211.01994.pdf">
<papertitle>lilGym: Natural Language Visual Reasoning with Reinforcement Learning</papertitle>
</a>
<br>
<a href="https://annshin.github.io/">Anne Wu</a>,
<a href="http://www.cs.umd.edu/~kdbrant/">Kianté Brantley</a>,
<strong>Noriyuki Kojima</strong>,
<a href="https://yoavartzi.com/">Yoav Artzi</a>
<br>
ACL, 2023
<br>
<a href="https://lil-lab.github.io/lilgym/">project page</a> /
<a href="https://arxiv.org/pdf/2211.01994.pdf">arXiv</a> /
<a href="https://github.com/lil-lab/lilgym">code</a>
<p> Presenting a new benchmark for language-conditioned reinforcement learning in visual environments.</p>
</td>
</tr>
<!-- Markup-to-Image Diffusion Models with Scheduled Sampling -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/molecules_rendering.gif" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='images/molecules_rendering.gif' width="160" style=padding-top:20px;>
</div>
<script type="text/javascript">
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mipnerf_stop()
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2210.05147">
<papertitle>Markup-to-Image Diffusion Models with Scheduled Sampling</papertitle>
</a>
<br>
<a href=https://yuntiandeng.com//>Yuntian Deng</a>,
<strong>Noriyuki Kojima</strong>,
<a href="http://rush-nlp.com/">Alexander Rush</a>
<br>
ICLR, 2022
<br>
<a href="https://huggingface.co/spaces/yuntian-deng/latex2im">demo</a>
/
<a href="https://arxiv.org/abs/2210.05147">arXiv</a>
<p> Studying diffusion models for markup-to-image generation.</p>
</td>
</tr>
<!-- Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="videos/tacl2021.mp4" type="video/mp4">
</video></div>
<img src='images/tacl2021.jpg' width="160" style=padding-top:32px;>
</div>
<script type="text/javascript">
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mipnerf_stop()
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior</papertitle>
<br>
<strong>Noriyuki Kojima</strong>,
<a href="http://alanesuhr.com/">Alane Suhr</a>,
<a href="https://yoavartzi.com/">Yoav Artzi</a>
<br>
<em>TACL (presented at EMNLP)</em>, 2021 <font color="orange"><strong>(Oral)</strong></font>
<br>
<a href="https://lil.nlp.cornell.edu/cerealbar/">project page</a>
/
<a href="https://arxiv.org/abs/2108.04812">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=KkgIMPTS7H0&t=1s">video</a>
/
<a href="https://github.com/lil-lab/cerealbar_generation">code</a>
<p></p>
<p>Studying continual learning for natural language instruction generation by observing human users' instruction execution in a collaborative game.</p>
</td>
</tr>
<!-- What is Learned in Visually Grounded Neural Syntax Acquisition -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/acl2020.gif" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='images/acl2020.gif' width="160" style=padding-top:20px;>
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2005.01678">
<papertitle>What is Learned in Visually Grounded Neural Syntax Acquisition</papertitle>
</a>
<br>
<strong>Noriyuki Kojima</strong>,
<a href=http://www.cs.cornell.edu/~hadarelor/>Hadar Averbuch-Elor</a>,
<a href="http://rush-nlp.com/">Alexander Rush</a>,
<a href="https://yoavartzi.com/">Yoav Artzi</a>
<br>
<em>ACL</em>, 2020
<br>
<a href="https://arxiv.org/abs/2005.01678">arXiv</a>
/
<a href="https://www.dropbox.com/s/dx1ecbvdsyvd0cl/Presentation.mov?dl=0">video</a>
/
<a href="https://github.com/lil-lab/vgnsl_analysis">code</a>
<p></p>
<p>Exploring what is learned by the unsupervised vision and language constituency parser via extreme simplification of model architectures.</p>
</td>
</tr>
<!-- OASIS: A Large-Scale Dataset for Single Image 3D in the Wild -->
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<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="videos/oasis.gif" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='images/oasis.gif' width="160" style=padding-top:20px;>
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2007.13215">
<papertitle>OASIS: A Large-Scale Dataset for Single Image 3D in the Wild</papertitle>
</a>
<br>
<a href="http://www-personal.umich.edu/~wfchen/">Weifeng Chen</a>,
<a href="https://jasonqsy.github.io/">Shengyi Qian</a>,
<a href="https://davidfan.io/">David Fan</a>,
<strong>Noriyuki Kojima</strong>,
Max Hamilton,
<a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>
<br>
<em>CVPR</em>, 2020
<br>
<a href="https://oasis.cs.princeton.edu/">project page</a>
/
<a href="https://arxiv.org/abs/2007.13215">arXiv</a>
/
<a href="https://oasis.cs.princeton.edu/download">data</a>
/
<a href="https://github.com/princeton-vl/oasis">code</a>
<p></p>
<p>Presenting Open Annotations of Single Image Surfaces (OASIS): a dataset for images in the wild with dense annotations of detailed 3D geometry.</p>
</td>
</tr>
<!-- Representing Movie Characters in Dialogues -->
<tr onmouseout="mipnerf_stop()" onmouseover="mipnerf_start()">
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<div class="one">
<div class="two" id='mipnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/conll19.jpg" type="video/mp4" style=padding-top:20px;>
</video></div>
<img src='images/conll19.jpg' width="160" style=padding-top:20px;>
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://aclanthology.org/K19-1010.pdf">
<papertitle>Representing Movie Characters in Dialogues</papertitle>
</a>
<br>
<a href="http://web.eecs.umich.edu/~mazab/">Mahmoud Azab</a>,
<strong>Noriyuki Kojima</strong>,
<a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>,
<a href="https://web.eecs.umich.edu/~mihalcea/">Rada Mihalcea</a>
<br>
<em>CoNLL</em>, 2019 <font color="orange"><strong>(Oral)</strong></font>
<br>
<a href="https://aclanthology.org/K19-1010.pdf">paper</a>
/
<a href="http://web.eecs.umich.edu/~mihalcea/downloads/CharacterRelatedness.zip">data</a>
<p></p>
<p>Representing character names by adding social networks of discourse in embedding objectives.</p>
</td>
</tr>
<!-- To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments -->
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<source src="videos/arxiv20.mp4" type="video/mp4">
</video></div>
<img src='images/mipnerf_ipe_yellow.png' width="160">
</div>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1907.11770">
<papertitle>To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments</papertitle>
</a>
<br>
<strong>Noriyuki Kojima</strong>,
<a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>
<br>
<em>Preprint</em>, 2019
<br>
<a href="https://arxiv.org/abs/1907.11770">arXiv</a>
<p></p>
<p> Comparing learning-based methods and classical methods for navigation in virtual environments. Classical methods > learning-based methods in high-level, but learning-based methods learned useful regularities. </p>
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<a href="https://arxiv.org/abs/1809.08761">
<papertitle>Speaker naming in movies</papertitle>
</a>
<br>
<a href="http://web.eecs.umich.edu/~mazab/">Mahmoud Azab</a>,
Mingzhe Wang,
<a href="http://maxosmith.com/">Max Smith</a>,
<strong>Noriyuki Kojima</strong>,
<a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>,
<a href="https://web.eecs.umich.edu/~mihalcea/">Rada Mihalcea</a>
<br>
<em>NAACL</em>, 2018
<br>
<a href="https://arxiv.org/abs/1809.08761">arXiv</a>
/
<a href="https://lit.eecs.umich.edu/posters/Azab_poster.pdf">poster</a>
/
<a href="https://lit.eecs.umich.edu/files/downloads/speakerNaming.zip">data</a>
<p></p>
<p> Proposing a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. </p>
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<a href="https://link.springer.com/chapter/10.1007/978-3-319-46484-8_42">
<papertitle>Structured matching for phrase localization</papertitle>
</a>
<br>
Mingzhe Wang,
<a href="http://web.eecs.umich.edu/~mazab/">Mahmoud Azab</a>,
<strong>Noriyuki Kojima</strong>,
<a href="https://www.cs.princeton.edu/~jiadeng/">Jia Deng</a>,
<a href="https://web.eecs.umich.edu/~mihalcea/">Rada Mihalcea</a>
<br>
<em>ECCV</em>, 2016
<br>
<a href="https://link.springer.com/chapter/10.1007/978-3-319-46484-8_42">paper</a>
/
<a href="http://www.eccv2016.org/files/posters/P-3C-40.pdf">poster</a>
/
<a href="https://github.com/princeton-vl/structured-matching">code</a>
<p></p>
<p> Proposing structured matching of phrases and regions that encourages the semantic relations between phrases to agree with the visual relations. </p>
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<td style="padding:20px;width:25%;vertical-align:middle"><img src="images/reviewer.jpg" width="160"></td>
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Reviewer: ACL2020, NAACL 2021, <a href="https://2021.aclweb.org/blog/reviewer-list/">ACL2021</a> <font color="red"><strong>(Outstanding Reviewer)</strong></font>, ACL Rolling Review 2021 - 2022 and so forth
<br><br>
<a href="https://alvr-workshop.github.io/">Program Committee: 2nd Workshop on Advances in Language and Vision Research (ALVR)</a>
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Last updated: August 2021 <br>
The template was borrowed from <a href="https://jonbarron.info/">Jon Barron</a>'s <a href="https://github.com/jonbarron/jonbarron_website">implementaion</a>.
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