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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
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<title>JonathanMunro</title>
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<span class="d-block d-lg-none">Jonathan Munro</span>
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<section class="resume-section p-3 p-lg-5 d-flex align-items-center" id="about">
<div class="w-100">
<h1 class="mb-0">Jonathan
<span class="text-primary">Munro</span>
</h1>
<div class="subheading mb-5">University of Bristol·
<a href="mailto:name@email.com">jonathan.munro@bristol.ac.uk</a>
</div>
<p class="lead mb-5">I am a final year PhD student at University of Bristol supervised Dr Dima Damen. My interests include fine-grained video understanding and transfer learning with a focus on domain adaptation for action recognition.</p>
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<section class="resume-section p-3 p-lg-5 d-flex justify-content-center" id="projects">
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<h2 class="mb-5">Projects</h2>
<!--<a href="https://jonmun.github.io/mmsada/">-->
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<a href="https://jonmun.github.io/mmsada/">
<h3 class="mb-0">Multi-Modal Domain Adaptation for Fine-grained Action Recogntition</h3>
</a>
<p> Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. However, these approaches have not explored the multi-modal nature of video within each domain. In this work we exploit the correspondence of modalities as a self-supervised alignment approach for UDA in addition to adversarial alignment. We test our approach on three kitchens from our large-scale dataset, EPIC-Kitchens, using two modalities commonly employed for action recognition: RGB and Optical Flow. We show that multi-modal self-supervision alone improves the performance over source-only training by 2.4 on average. We then combine adversarial training with multi-modal self-supervision, showing that our approach outperforms other UDA methods by 3%.
</p>
<div style="width: 100%; max-width:600px;">
<a href="https://jonmun.github.io/mmsada/">
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</a>
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</div>
<!--<a href="https://epic-kitchens.github.io/2021">-->
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<div class="resume-content">
<a href="https://epic-kitchens.github.io/2021">
<h3 class="mb-0">EPIC-KITCHENS-100</h3>
</a>
<p>The extended largest dataset in first-person (egocentric) vision; multi-faceted, audio-visual, non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities in the kitchen over multiple days. Annotations are collected using a novel 'Pause-and-Talk' narration interface.</p>
<div style="width: 100%; max-width:600px;">
<a href="https://epic-kitchens.github.io/2021">
<video autoplay muted loop width="100%">
<source src='img/03x03_videoWall.webm' style="width: 100%" type="video/webm" />
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</div>
</div>
</div>
</section>
<section class="resume-section p-3 p-lg-5 d-flex justify-content-center" id="publications">
<div class="w-100">
<h2 class="mb-5">Publications</h2>
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<h3 class="mb-0">Recaling egocentric vision</h3>
<div class="subheading mb-3">ArXiv</div>
<p>Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray</p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">2020</span>
</div>
</div>
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<h3 class="mb-0">The epic-kitchens dataset: Collection, challenges and baselines</h3>
<div class="subheading mb-3">IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</div>
<p>Dima Damen, Hazel Doughty, Giovanni Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray</p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">2020</span>
</div>
</div>
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<h3 class="mb-3">Multi-modal domain adaptation for fine-grained action recogntition</h3>
<div class="subheading mb-3">Computer Vision and Pattern Recognition(CVPR)</div>
<p>Jonathan Munro, Dima Damen</p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">2020</span>
</div>
</div>
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<h3 class="mb-0">Scaling egocentric vision: The EPIC-KITCHENS dataset</h3>
<div class="subheading mb-3">European Conference on Computer Vision (ECCV)</div>
<p>Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray</p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">2018</span>
</div>
</div>
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<h2 class="mb-5">Interships</h2>
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<h3 class="mb-0">Data Science Intern</h3>
<div class="subheading mb-3">Intent HQ</div>
<p></p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">Summer 2017</span>
</div>
</div>
<div class="resume-item d-flex flex-column flex-md-row justify-content-between mb-5">
<div class="resume-content">
<h3 class="mb-0">Applications Engineering Intern</h3>
<div class="subheading mb-3">ARM</div>
<p></p>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">Summer 2015</span>
</div>
</div>
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<div class="resume-content">
<h3 class="mb-0">Applications Engineering Intern</h3>
<div class="subheading mb-3">ARM</div>
<p></p>
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<span class="text-primary">Summer 2014</span>
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<h2 class="mb-5">Education</h2>
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<h3 class="mb-0">University of Bristol</h3>
<div class="subheading mb-3">PhD Computer Science</div>
</div>
<div class="resume-date text-md-right">
<span class="text-primary">August 2017 - Now</span>
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<h3 class="mb-0">University of Bristol</h3>
<div class="subheading mb-3">MEng Computer Science and Electronics</div>
<p>First</p>
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EPSRC Scholarship 2017-2021</li>
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