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---
title: "EPIC-KITCHENS Dataset"
layout: default
---
<!-- About -->
<section class="bg-light" id="about">
<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-uppercase">News</h2>
<h5 class="text-muted" style="text-align:left;">
<ul>
<li>Our paper <a href="https://arxiv.org/abs/2006.13256">Rescaling Egocentric Vision</a> is accepted for publication at IJCV. Camera-ready version now online</li>
<li>14 July 2021: Leaderboards are open now for testing models until 25 Nov - max 50 submissions.</li>
<li>July 2021: <a href="./Reports/EPIC-KITCHENS-Challenges-2021-Report.pdf">2021 Technical report is available online</a></li>
<li>June 2021: Winners announced on 20 June. <a href="https://youtu.be/FSn8yCbpcc4">Watch workshop session</a> and <a href="#results">check the results</a></li>
<li>May 2021: Challenge leaderboard deadline has been extended to June 1st for all challenges. All other dates remain fixed.</li>
<li>Apr 2021: Celebrating 3 years of EPIC-KITCHENS with a <a href="https://engineering.blogs.bristol.ac.uk/epic-kitchens-machine-learning-ai-computer-vision-reality/#.YIvLfOEUalk.twitter">general audience blog</a></li>
<li>10 Sep 2020: Erratum in pre-extracted frames for 2 videos - <a href="#downloadFiles">please read here</a></li>
<li>23 Aug 2020: Leaderboards are now open.</li>
<li>1st of July 2020: EPIC-KITCHENS-100 is now Released! <a href="https://t.co/lnz9JI486s?amp=1">Watch release webinar recording</a></li>
<li>Watch the dataset's <a href="https://www.youtube.com/watch?v=8IzkrWAfAGg" target="_blank">trailer</a> and <a href="https://www.youtube.com/watch?v=MUlyXDDzbZU&t=4s">video demonstration</a> on YouTube</li>
</ul>
</h5>
</div>
</div>
<div class="row">
<!-- news column -->
<div class="col-md-4">
<h4 class="service-heading">What is EPIC-KITCHENS-100?</h4>
<p class="text-muted">The <i>extended</i> <b>largest dataset in first-person (egocentric) vision</b>; multi-faceted, audio-visual, <b>non-scripted</b> 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>
<!-- characteristics column -->
<div class="col-md-4">
<h4 class="service-heading">Characteristics</h4>
<ul class="text-muted">
<li class="text-muted">45 kitchens - 4 cities</li>
<li class="text-muted">Head-mounted camera</li>
<li class="text-muted"><b>100</b> hours of recording - Full HD</li>
<li class="text-muted">20M frames</li>
<li class="text-muted">Multi-language narrations</li>
<li class="text-muted">90K action segments</li>
<li class="text-muted">20K unique narrations</li>
<li class="text-muted">97 verb classes, 300 noun classes</li>
<li class="text-muted">5 challenges</li>
</ul>
</div>
<!-- udated column -->
<div class="col-md-4">
<h4 class="service-heading">Previous versions...</h4>
<ul class="text-muted">
<li class="text-muted">2021 Challenges: <a href="https://epic-kitchens.github.io/2021.html#results">Results</a>, <a href="./Reports/EPIC-KITCHENS-Challenges-2021-Report.pdf">Tech Report</a> [<a href="./Reports/2021-bibtex.txt">Bibtex</a>]</li>
<li class="text-muted">The previous version of the dataset (55 hours) was released in April 2018</li>
<li class="text-muted">Refer to <a href="2020-55.html">EPIC-KITCHENS-55</a> for details</li>
<li class="text-muted">2020 Challenges: <a href="https://epic-kitchens.github.io/2020-55.html#results">Results</a>, <a href="./Reports/EPIC-KITCHENS-Challenges-2020-Report.pdf">Tech Report</a> [<a href="./Reports/2020-bibtex.txt">Bibtex</a>]</li>
<li class="text-muted">2019 Challenges: <a href="https://epic-kitchens.github.io/2019.html#results">Results</a>, <a href="Reports/EPIC-Kitchens-Challenges-2019-Report.pdf">Tech Report</a> [<a href="./Reports/2019-bibtex.txt">Bibtex</a>]</li>
<li class="text-muted">EPIC-KITCHENS-55 leaderboards remain open until the end of 2020</li>
</ul>
</div>
</div>
<!-- video banner row -->
<div class="row">
<div class="col-lg-12">
<video autoplay muted loop width="100%">
<source src="{{ site.baseurl }}/static/videos/03x03_videoWall.webm" type="video/webm">
<source src="{{ site.baseurl }}/static/videos/03x03_videoWall.mp4" type="video/mp4">
<source src="{{ site.baseurl }}/static/videos/03x03_videoWall.mp4" type="video/mp4">
Sorry, we cannot display the EPIC-KITCHENS-100 video wall as
your browser doesn't support HTML5 video.
</video>
</div>
</div>
</div>
</section>
<!-- Stats -->
<section id="stats">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">EPIC-KITCHENS-100 Stats and Figures</h2>
<h3 class="section-subheading text-muted">Some graphical representations of our dataset and annotations</h3>
</div>
</div>
<div class="row justify-content-md-center text-center">
<div class="col-md centered" style="padding:1rem;">
<img src="{{ site.baseurl }}/static/img/stats-figures/verb_categories.png" style="width: 100%" class="img-responsive"/>
</div>
</div>
<div class="row justify-content-md-center text-center">
<div class="col-md centered" style="padding:1rem;">
<img src="{{ site.baseurl }}/static/img/stats-figures/noun_categories.png" style="width: 100%" class="img-responsive"/>
</div>
</div>
<div class="row justify-content-md-center text-center">
<div class="col-md-6 centered" style="padding:1rem;">
<img src="{{ site.baseurl }}/static/img/stats-figures/pipeline.png" style="width: 100%" class="img-responsive"/>
<h4>Annotation Pipeline</h4>
</div>
<div class="col-md-6 centered" style="padding:1rem; vertical-align:bottom">
<!-- TO ADD GRAPH: replace div below, ex: above <img> tag -->
<img src="{{ site.baseurl }}/static/img/stats-figures/masks.png" style="width: 100%" class="img-responsive"/>
<h4>Automatic Annotations</h4>
</div>
</div>
<!-- <div class="row justify-content-md-center text-center">
<div class="col-md-4 centered" style="padding:1rem;">
<div id="graph4" style="width: 100%" class="img-responsive"></div>
<h4>Resolution</h4>
</div>
<div class="col-md-4 centered" style="padding:1rem;">
<div id="graph5" style="width: 100%" class="img-responsive"></div>
<h4>Number of Frames</h4>
</div>
<div class="col-md-4 centered" style="padding:1rem;">
<div id="graph6" style="width: 100%" class="img-responsive"></div>
<h4>Total number of hours</h4>
</div>
</div>
<div class="row justify-content-md-center text-center">
<div class="col-md-4 centered" style="padding:1rem;">
<div id="graph7" style="width: 100%" class="img-responsive"></div>
<h4>Number of annotators<br/>used per video</h4>
</div>
<div class="col-md-4 centered" style="padding:1rem;">
<div id="graph8" style="width: 100%" class="img-responsive"></div>
<h4>Splits</h4>
</div>
</div> -->
<!-- <div class="col-md-6">
<div class="card" style="border: solid 2px; background-color: #373435ff; margin-bottom:5px;">
<h1 style=" color: white; text-decoration: underline; text-decoration-color: #ed323eff;"> Baseline Models </h1>
<div id="graph9"></div>
</div>
<div class="card" style="border: solid 2px; background-color: #ed323eff;">
<h1 style=" color: white; text-decoration: underline; text-decoration-color: #373435ff;"> State of the Art Results </h1>
<div id="graph10"></div>
</div>
</div> -->
</div>
</section>
<section class="bg-light" id="downloads">
<div class="container">
<div class="row">
<div class="col-md-12 text-center">
<h2 class="section-heading text-uppercase">Download</h2>
<h3 class="section-subheading text-muted">Dataset publicly available for research purposes</h3>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading" id="downloadFiles">Data and Download Script</h4>
<hr/>
<p><b>Erratum [Important]:</b> We have recently detected an error in our pre-extracted RGB and Optical flow frames for two videos in our dataset. This does not affect the videos themselves or any of the annotations in this github. However, if you've been using our pre-extracted frames, you can fix the error at your end by <a href="https://github.com/epic-kitchens/epic-kitchens-100-annotations/blob/master/README.md#erratum">following the instructions in this link.</a></p><hr/>
<p>
Extended Sequences (+RGB Frames, Flow Frames, Gyroscope + accelerometer data): Available at <a href="http://dx.doi.org/10.5523/bris.2g1n6qdydwa9u22shpxqzp0t8m">Data.Bris servers (740GB zipped)</a> or <a href="https://academictorrents.com/details/cc2d9afabcbbe33686d2ecd9844b534e3a899f4b">via Academic Torrents</a>
</p>
<p>
Original Sequences (+RGB and Flow Frames): Available at <a href="http://dx.doi.org/10.5523/bris.3h91syskeag572hl6tvuovwv4d">Data.Bris servers (1.1TB zipped)</a> or <a href="https://academictorrents.com/details/d08f4591d1865bbe3436d1eb25ed55aae8b8f043">via Academic Torrents</a>
</p>
<p> Automatic annotations (masks, hands and objects): Available for download at <a href="https://data.bris.ac.uk/data/dataset/3l8eci2oqgst92n14w2yqi5ytu">Data.Bris server (10 GB)</a>. We also have two Repos that will allow you to visualise and utilise these automatic annotations for <a href="https://github.com/epic-kitchens/epic-kitchens-100-object-masks">hand-objects</a> as well as <a href="https://github.com/epic-kitchens/epic-kitchens-100-object-masks">masks</a>.</p>
<p> We also offer a <a href="https://github.com/epic-kitchens/epic-kitchens-download-scripts">python script to download</a> various parts of the dataset</p>
<h4 class="section-subheading">Annotations and Pipeline</h4>
<p>All annotations (Train/Val/Test) for all challenges are available at <a href="https://github.com/epic-kitchens/epic-kitchens-100-annotations">EPIC-KITCHENS-100-annotations repo</a></p>
<p>Code to visualise and utilise automatic annotations is available for both <a href="https://github.com/epic-kitchens/epic-kitchens-100-object-masks">object masks</a> and <a href="https://github.com/epic-kitchens/epic-kitchens-100-hand-object-bboxes">hand-object detections</a>.</p>
<p>The EPIC Narrator, used to collect narrations for EPIC-KITCHENS-100 is open-sourced at <a href="https://github.com/epic-kitchens/epic-kitchens-100-narrator">EPIC-Narrator repo</a></p>
<section>
<h4 class="section-subheading">Publication(s)</h4>
<p>Cite our IJCV 2021 paper
<a href="https://link.springer.com/content/pdf/10.1007/s11263-021-01531-2.pdf">PDF</a> or <a href="https://arxiv.org/abs/2006.13256">Arxiv</a>:</p>
<pre class="bibtex"><code>@ARTICLE{Damen2021RESCALING,
title={Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and and Furnari, Antonino
and Ma, Jian and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
journal = {International Journal of Computer Vision (IJCV)},
year = {2021},
Url = {https://doi.org/10.1007/s11263-021-01531-2}
} </code></pre>
<p>Additionally, cite the original paper
(<a href="https://arxiv.org/abs/1804.02748">available now on Arxiv</a> and
<a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Dima_Damen_Scaling_Egocentric_Vision_ECCV_2018_paper.html">the CVF</a>):</p>
<pre class="bibtex"><code>
@INPROCEEDINGS{Damen2018EPICKITCHENS,
title={Scaling Egocentric Vision: The EPIC-KITCHENS Dataset},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and Fidler, Sanja and
Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
booktitle={European Conference on Computer Vision (ECCV)},
year={2018}
} </code></pre>
<p>An extended journal paper is avaliable at:
(available now on <a href="https://ieeexplore.ieee.org/document/9084270">IEEE</a> and a <a href="https://arxiv.org/abs/2005.00343">preprint on Arxiv</a> and ):</p>
<pre class="bibtex"><code>
@ARTICLE{Damen2021PAMI,
title={The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and Fidler, Sanja and
Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2021},
volume={43},
number={11},
pages={4125-4141},
doi={10.1109/TPAMI.2020.2991965}
} </code></pre>
</section>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Disclaimer </h4>
<p>EPIC-KITCHENS-55 and EPIC-KITCHENS-100 were collected as a tool for research in computer vision, however, it is worth noting that the dataset may have unintended biases (including those of a societal, gender or racial nature).</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Copyright <img alt="Creative Commons License" style="border-width:1px;float:left;margin-right:15px;margin-bottom:0px;" src="https://i.creativecommons.org/l/by-nc/3.0/88x31.png"/></h4>
<p>
All datasets and benchmarks on this page are copyright by us and published under the <a rel="license" href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International</a> License. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
</p>
</div>
</div>
</div>
</section>
<!-- Challenges -->
<section id="challenges">
<div class="container">
<div class="row">
<div class="col-md-12 text-center">
<h2 class="section-heading text-uppercase">EPIC-KITCHENS-100 2021 Challenges</h2>
<h3 class="section-subheading text-muted">Challenge and Leaderboard Details with links to Codalab Leaderboards</h3>
</div>
</div>
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<div class="col-md-12">
2021 Challenges are now closed, but the leaderboards remain open for testing models and paper submissions. Check the <a href="#results">results of the 2021 chalenge results below</a>
<h4 class="subheading">EPIC-Kitchens 2021 Open Testing - Dates</h4>
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July 14, 2021
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EPIC-Kitchens Challenges 2021 are open for testing
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Nov 25th 2021,
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All leaderboards are closed (after CVPR supplementary deadline) in preparation for the 2022 challenges
</div>
</div>
<h4 class="subheading">EPIC-Kitchens 2021 Challenges - (Now completed)</h4>
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<s>Aug 23rd, 2020</s>
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EPIC-Kitchens Challenges 2021 Launched alongisde EPIC@ECCV Workshop
</div>
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<s>Jan 12th 2020</s>,
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All leaderboards are open
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<s>June 01, 2021 [Extended]</s>
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Server Submission Deadline at 23:59:59 GMT
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<s>Jun 4, 2021</s>
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Deadline for Submission of Technical Reports on <a href="https://cmt3.research.microsoft.com/EPICCVPR2021">CMT</a> (see guidelines below)
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<s>June 20, 2021</s>
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Results announced at <a href="https://eyewear-computing.org/EPIC_CVPR21/">EPIC@CVPR2021 workshop</a>
</div>
</div>
</div>
</div>
<br/>
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<h4 class="subheading">Open Testing Phase Guidelines</h4>
<p>The five leaderboards are available for testing. This is not a formal challenge. Please revisit the website next January for 2022 challenge guideilnes. The CodaLab server pages detail submission format and evaluation metrics. </p>
<p>To <b>submit to any of the five competitions</b>, you need to register an account for that challenge using a valid institute (university/company) email address. A single registration per research team is allowed. We perform a manual check for each submission, and expect to accept registrations within 2 working days.</p>
<p>For all challenges the maximum submissions per day is limited to 1, and the overall maximum number of submissions per team is limited to 50 overall, submitted once a day. This includes any failed submissions due to formats - please do not contact us to ask for increasing this limit.</p>
<p>To <b>submit</b> your results, follow the JSON submission format, upload your results and give time for the evaluation to complete (in the order of several minutes). <b>Note our new rules on declaring the supervision level, given our proposed scale, for each submission.</b> After the evaluation is complete, the results automatically appear on the public leaderboards but you are allowed to withdraw these at any point in time.</p>
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<!-- <h4 class="subheading">Challenges Guidelines</h4>
<p>The five challenges below and their test sets and evaluation servers are available via CodaLab. The leaderboards will decide the winners for each individual challenge. For each challenge, the CodaLab server page details submission format and evaluation metrics. </p>
<p>To <b>enter any of the five competitions</b>, you need to register an account for that challenge using a valid institute (university/company) email address. A single registration per research team is allowed. We perform a manual check for each submission, and expect to accept registrations within 2 working days.</p>
<p>For all challenges the maximum submissions per day is limited to 1, and the overall maximum number of submissions per team is limited to 50 overall, submitted once a day. This includes any failed submissions due to formats - please do not contact us to ask for increasing this limit.</p>
<p>To <b>submit</b> your results, follow the JSON submission format, upload your results and give time for the evaluation to complete (in the order of several minutes). <b>Note our new rules on declaring the supervision level, given our proposed scale, for each submission.</b> After the evaluation is complete, the results automatically appear on the public leaderboards but you are allowed to withdraw these at any point in time.</p>
<p>To <b>participate</b> in the challenge, you need to have your results on the public leaderboard, along with an informative team name (that represents your institute or the collection of institutes participating in the work), as well as brief information on your method. You are also required to submit a report on your method (in the form of 2-6 pages) to the EPIC@CVPR workshop by June 4th, detailing your entry's technical details.</p>
<p>To <b>submit your technical report</b>, use the <a href="hhttp://cvpr2021.thecvf.com/node/33#submission-guidelines">CVPR 2021 camera ready author kit (no blind submission)</a>, and submit a report of 2-6 pages inclusive of any references to the EPIC@CVPR2021 CMT3 website (<a href="https://cmt3.research.microsoft.com/EPICCVPR2021">Link here</a>). Please select the track "EPIC-Kitchens 2021 Challenges - Technical Papers" when submitting your pdf. These technical reports will be combined into an overall report of the EPIC-Kitchens challenges.</p>
<p>Make the most of the starter packs available with the challenges, and should you have any questions, please use our info email <a href="mailto:uob-epic-kitchens@bristol.ac.uk">uob-epic-kitchens@bristol.ac.uk</a></p> -->
</div>
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<h4 class="subheading">Frequently Asked Questions</h4>
<ul>
<li>
<b>Q.</b> Who is allowed to participate?<br/>
<b>A.</b> Any researcher, whether in academia or industry, is invited to participate in the EPIC-KITCHENS-100 Challenges and open testing phase. We only request a valid official email address, associated with an institution, for registraton. This ensures we limit the number of submissions per team. Do not use your personal email for registration. You registration request will be declined without further explanation.
</li>
<!-- <li>
<b>Q.</b> Can I participate in more than one challenge? Do I need to register separately for each challenge?<br/>
<b>A.</b> Yes, and yes. You can participate in all challenges but you need to register separately for each. Winners for each challenge will be announced and there will be no 'overall' winner across challenges.
</li>-->
<li>
<b>Q.</b> Can I get additional submission limits to debug my file format?<br/>
<b>A.</b> No. Please check your format in advance. We do not offer additional allowance for submission failures.
</li>
<!-- <li>
<b>Q.</b> Can I participate in the challenge but not submit a report describing my method?<br/>
<b>A.</b> No. Entries to the challenge will only be considered if a technical report is submitted on time. This should not affect later publications of your method if you restrict your report to 4 pages including references.
</li>-->
<!-- <li>
<b>Q.</b> What happens after the server closes?<br/>
<b>A.</b> We will close the test server after the deadline and until the workshops, but open it again later for normal submissions of future papers.
</li> -->
<!-- <li>
<b>Q.</b> Are there any prizes given?<br/>
<b>A.</b> There are no monetary prizes. Certificates will be awarded.
</li> -->
<li>
<b>Q.</b> What is the submission limit?<br/>
<b>A.</b> Once a day, with a maximum of 50 submissions.
</li>
<li>
<b>Q.</b> Can you give me access to pre-trained models of baselines?<br/>
<b>A.</b> Yes, check the details of this per challenge.
</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="subheading">Challenges/Leaderboard Details</h4>
<ul>
<li><a href="#challenge-action-recognition">Action Recognition</a></li>
<li><a href="#challenge-action-detection">Action Detection</a></li>
<li><a href="#challenge-action-anticipation">Action Anticipation</a></li>
<li><a href="#challenge-domain-adaptation">Domain Adaptation for Action Recognition</a></li>
<li><a href="#challenge-action-retrieval">Multi-Instance Retrieval</a></li>
</ul>
<p class="text-muted">
<b>Splits. </b> The dataset is split in train/validation/test sets, with a ratio of roughly 75/10/15. <br/>
The action recognition, detection and anticipation challenges use all the splits. <br/>
The unsupservised domain adaptation and action retrieval challenges use different splits as detailed below. <br/>
You can download all the necessary annotations <a href="https://github.com/epic-kitchens/epic-kitchens-100-annotations" target="_blank">here</a>. <br/>
You can find more details about the splits in <a href="https://arxiv.org/pdf/2006.13256.pdf" target="_blank">our paper</a>.
</p>
<p class="text-muted">
<b>Evaluation. </b> All challenges are evaluated considering all segments in the Test split.
The action recognition and anticipation challenges are additionally evaluated considering unseen participants and tail classes. These are automatically evaluated in the scripts and you do not need to do anything specific to report these.<br/>
<b>Unseen participants. </b> The validation and test sets contain participants that are not present in the train set.
There are 2 unseen participants in the validation set, and another 3 participants in the test set.
The corresponding action segments are 1,065 and 4,110 respectively. <br/>
<b>Tail classes. </b> These are the set of smallest classes whose instances account for 20% of the total number of instances in
training. A tail action class contains either a tail verb class or a tail noun class.
<br/><br/>
</p>
<section class="challenge" id="challenge-action-recognition">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Action Recognition</h5>
<p class="text-muted">
<b>Task. </b>
Assign a (verb, noun) label to a trimmed segment.<br/>
<b>Training input (strong supervision). </b> A set of trimmed action segments, each annotated with a (verb, noun) label. <br/>
<b>Training input (weak supervision). </b> A set of <i>untrimmed videos</i>, each annotated with a list of (timestamp, verb, noun) labels.
Note that for each action you are given a <i>single, roughly aligned</i> timestamp, i.e. one timestamp located
around the action. Timestamps may be located over background frames or frames belonging to another action. <br/>
<b>Testing input. </b> A set of trimmed unlabelled action segments. <br/>
<b>Splits. </b> Train and validation for training, evaluated on the test split. <br/>
<b>Evaluation metrics. </b> Top-1/5 accuracy for verb, noun and action (verb+noun), calculated for all segments as well as
unseen participants and tail classes.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C1-Action-Recognition">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C1-Action-Recognition">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25923#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/25923#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25923">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/25923#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/ar.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-action-detection">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Action Detection</h5>
<p class="text-muted">
<b>Task. </b>
Detect the start and the end of each action in an <i>untrimmed</i> video. Assign a (verb, noun) label to each
detected segment. <br/>
<b>Training input. </b> A set of trimmed action segments, each annotated with a (verb, noun) label. <br/>
<b>Testing input. </b> A set of <i>untrimmed</i> videos. <u>Important:</u> You are not allowed to use the knowledge of trimmed segments in the test set when reporting for this challenge.<br/>
<b>Splits. </b> Train and validation for training, evaluated on the test split. <br/>
<b>Evaluation metrics. </b> Mean Average Precision (mAP) @ IOU 0.1 to 0.5.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C2-Action-Detection">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C2-Action-Detection">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25926#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/25926#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25926">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/25926">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/ad.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-action-anticipation">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Action Anticipation</h5>
<p class="text-muted">
<b>Task. </b>
Predict the (verb, noun) label of a future action observing a segment preceding its occurrence. <br/>
<b>Training input. </b> A set of trimmed action segments, each annotated with a (verb, noun) label. <br/>
<b>Testing input. </b> During testing you are allowed to observe a segment that <i>ends</i> at least one second before
the start of the action you are testing on.<br/>
<b>Splits. </b> Train and validation for training, evaluated on the test split. <br/>
<b>Evaluation metrics. </b> Top-5 recall averaged for all classes, as defined <a href="https://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Furnari_Leveraging_Uncertainty_to_Rethink_Loss_Functions_and_Evaluation_Measures_for_ECCVW_2018_paper.pdf" target="_blank">here</a>,
calculated for all segments as well as unseen participants and tail classes.
<br/>
<!--
<b>Caveat. </b> You are not allowed to observe any frame after one second from the start of a testing action.
-->
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C3-Action-Anticipation">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C3-Action-Anticipation">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25925#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/25925#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/25925">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/25925">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/aa.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-domain-adaptation">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Unsupervised Domain Adaptation for Action Recognition</h5>
<p class="text-muted">
<b>Task. </b> Assign a (verb, noun) label to a trimmed segment, following the Unsupervised Domain Adaptation paradigm:
a labelled source domain is used for training, and the model needs to adapt to an unlabelled target domain. <br/>
<b>Training input. </b> A set of trimmed action segments, each annotated with a (verb, noun) label. <br/>
<b>Testing input. </b> A set of trimmed unlabelled action segments. <br/>
<b>Splits. </b> Videos recorded in 2018 (EPIC-KITCHENS-55) constitute the source domain,
while videos recorded for EPIC-KITCHENS-100's extension constitute the unlabelled target domain.
This challenge uses custom train/validation/test splits, which you can find
<a href="https://github.com/epic-kitchens/epic-kitchens-100-annotations#unsupervised-domain-adaptation-challenge" target="_blank">here</a>. <br/>
<b>Evaluation metrics. </b> Top-1/5 accuracy for verb, noun and action (verb+noun), on the target test set.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C4-UDA-for-Action-Recognition">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C4-UDA-for-Action-Recognition">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/26096#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/26096#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/26096">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/26096">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/uda.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-action-retrieval">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Multi-Instance Retrieval</h5>
<p class="text-muted">
<b>Tasks. </b> <i>Video to text</i>: given a query video segment, rank captions such that those with a higher rank are
more semantically relevant to the action in the query video segment.
<i>Text to video:</i> given a query caption, rank video segments such that those with a higher rank are more semantically relevant
to the query caption. <br/>
<b>Training input. </b> A set of trimmed action segments, each annotated with a caption.
Captions correspond to the narration in English from which the action segment was obtained. <br/>
<b>Testing input. </b> A set of trimmed action segments with captions. Important: You are not allowed to use the known correspondence in the Test set <br/>
<b>Splits. </b> This challenge has its own custom splits, available <a href="https://github.com/epic-kitchens/epic-kitchens-100-annotations/tree/master/retrieval_annotations">here</a>. <br/>
<b>Evaluation metrics. </b> normalised Discounted Cumulative Gain (nDCG) and Mean Average Precision (mAP).
You can find more details in <a href="https://arxiv.org/pdf/2006.13256.pdf" target="_blank">our paper</a>.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C5-Multi-Instance-Retrieval">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C5-Multi-Instance-Retrieval">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/26138#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/26138#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/26138">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/26138">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/aret.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
</div>
</div>
<!--
<section class="challenge" id="challenge-action-recognition">
<div class="row">
<div class="col-md-12">
<h4 class="subheading"><a href="https://competitions.codalab.org/competitions/20115">Action-Recognition Challenge</a></h4>
<p class="text-muted">
Given a trimmed action segment, the challenge is to classify the
segment into its action class composed of the pair of verb and noun
classes. To participate in this challenge, predictions for all
segments in the seen (S1) and unseen (S2) test sets should be
provided. For each test segment we require the confidence scores
for each verb and noun class.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/starter-kit-action-recognition">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">Get started with the
<a href="https://github.com/epic-kitchens/starter-kit-action-recognition">starter pack</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20115#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/20115#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20115#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/20115#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/stats-figures/challenge2.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-action-anticipation">
<div class="row">
<div class="col-md-12">
<h4 class="subheading">
<a href="https://competitions.codalab.org/competitions/20071">Action-Anticipation Challenge</a>
</h4>
<p class="text-muted">
Given an anticipation time, set to 1s before the action starts, the
challenge is to classify the future action into its action class
composed of the pair of verb and noun classes. To participate in
this challenge, predictions for all segments in the seen (S1) and
unseen (S2) test sets should be provided. For each test segment we
require the confidence scores for each verb and noun class.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20071#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/20071#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20071#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/20071#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/stats-figures/challenge3.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="challenge-object-detection">
<div class="row">
<div class="col-md-12">
<h4 class="subheading"><a href="https://competitions.codalab.org/competitions/20111">Object-Detection Challenge</a></h4>
<p class="text-muted">
This challenge focuses on object detection and localisation. Note
that our annotations only capture the ‘active’ objects pre-,
during- and post- interaction. To participate in the challenge
bounding box predictions with confidence scores should be submitted
for sampled frames from the seen (S1) and unseen (S2) test sets.
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20111#learn_the_details-submission-format">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://competitions.codalab.org/competitions/20111#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://competitions.codalab.org/competitions/20111#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://competitions.codalab.org/competitions/20111#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Sample qualitative results from the challenge's baseline</figcaption>
<img src="{{ site.baseurl }}/static/img/stats-figures/challenge1.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
-->
</div>
</section>
<!-- Team -->
<section class="bg-light" id="team">
<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-uppercase">The Team</h2>
<div class="text-muted">
<p> We are a group of researchers working in computer vision
from the <a href="http://www.bristol.ac.uk/">University
of Bristol</a> and <a href="http://www.unict.it/">University of
Catania</a>. The original dataset was collected in collaboration with <a href="https://www.cs.utoronto.ca/~fidler/">Sanja Fidler, University of Toronto</a>
</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-6">
<a href="http://www.bristol.ac.uk/">
<img src="{{ site.baseurl }}/static/img/universities/bristol-min.png" alt="" style="width:90%; margin-top:15px; margin-bottom:15px;">
</a>
</div>
<div class="col-md-6">
<a href="https://www.unict.it/en/">
<img src="{{ site.baseurl }}/static/img/universities/catania-min.png" alt="" style="width:90%; margin-bottom:5px;">
</a>
</div>
</div>
<div class="row">
<div class="col-md-6">
<div class="team-member">
<a href="http://dimadamen.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/dd-min.jpg"/>
<h4>Dima Damen</h4></a>
<h5>Principal Investigator</h5>
<h6 class="text-muted">University of Bristol, United Kingom</h6>
</div>
</div>
<div class="col-md-6">
<div class="team-member">
<a href="http://www.dmi.unict.it/farinella/">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/gmf-min.jpg" />
<h4>Giovanni Maria Farinella</h4></a>
<h5>Co-Investigator</h5>
<h6 class="text-muted">University of Catania, Italy</h6>
</div>
</div>
</div>
<div class="row">
<div class="col-md-3">
<div class="team-member">
<a href="http://www.davidemoltisanti.com/research">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/dm-min.jpg" />
<h4>Davide Moltisanti</h4></a>
<h5>(Apr 2017 - )</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
<h6 class="text-muted">(curr.) Nanyang Tech University</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="https://mwray.github.io/">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/mw-min.jpg" />
<h4>Michael Wray</h4></a>
<h5>(Apr 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="http://hazeldoughty.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/hd-min.jpg" />
<h4>Hazel Doughty</h4></a>
<h5>(Apr 2017 - )</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
<h6 class="text-muted">(curr.) University of Amsterdam</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="https://tobyperrett.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/tp-min.jpg" />
</a>
<h4>Toby Perrett</h4>
<h5>(Apr 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
</div>
<div class="row">
<div class="col-md-3">
<div class="team-member">
<a href="http://iplab.dmi.unict.it/furnari/">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/af-min.jpg" />
<h4>Antonino Furnari</h4></a>
<h5>(Jul 2017 - )</h5>
<h6 class="text-muted">University of Catania</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="https://jonmun.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/jm-min.jpg" />
<h4>Jonathan Munro</h4></a>
<h5>(Sep 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="https://ekazakos.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/vk-min.jpg" />
<h4>Evangelos Kazakos</h4></a>
<h5>(Sep 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="http://willprice.org">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/wp-min.jpg" />
<h4>Will Price</h4></a>
<h5>(Oct 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
</div>
<div class="row">
<div class="col-md-3">
<div class="team-member">
<a href="https://majian8.github.io/jianma/">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/jm2-min.jpg" />
<h4>Jian Ma</h4></a>
<h5>(Sep 2019 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div>
<div class="col-md-3">
<div class="team-member">
<a href="https://dwhettam.github.io/">