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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">
<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 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>16 Aug 2022: Annoncing <a href="https://epic-kitchens.github.io/VISOR">VISOR (Segmentations and Object Relations) annotations</a></li>
<li>31 July 2022: The <a href="./Reports/EPIC-KITCHENS-Challenges-2022-Report.pdf">2022 Challenges Report is now available</a></li>
<li>14 July 2022: Leaderboards are re-opened for the open testing phase until 21 Nov 2022.</li>
<li>3 July 2022: <a href="#results">Results of the 2022 challenges</a> are now public</li>
<li>1 June 2022: Leaderboards are now closed</li>
<li>17 Jan 2022: Leaderboards are open now for challenge phase until 1 June - max 50 submissions.</li>
<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>July 2021: <a href="./Reports/EPIC-KITCHENS-Challenges-2021-Report.pdf">2021 Technical report is available online</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 <b>large-scale 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">Refer to <a href="2020-55.html">EPIC-KITCHENS-55</a> for previous version</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>
</ul>
</div>
</div>
<!-- video banner row -->
</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 paper (Open Access 2021 - Published 2022):
<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{Damen2022RESCALING,
title={Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria 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 = {2022},
volume = {130},
pages = {33–55},
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. 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>
<p>For commercial licenses of EPIC-KITCHENS and any of its annotations, email us at <a href="mailto:[email protected]">[email protected]</a></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 2022 Challenges</h2>
<h3 class="section-subheading text-muted">Challenge Details with links to ★NEW★ Codalab Leaderboards</h3>
</div>
</div>
<div class="row">
<div class="col-md-12">
<!-- <b><font color="red">New</font></b> leaderboards are now open for the <b>challenge phase</b>. Check the <a href="./2021.html#results">results of the 2021 chalenge results below</a><br/>-->
2022 Challenges are now closed (<a href="./Reports/EPIC-KITCHENS-Challenges-2022-Report.pdf">read the report</a>), the leaderboards are open for testing models and paper submissions. Check the <a href="#results">results of the 2022 chalenge results below</a>
<h4 class="subheading">EPIC-Kitchens 2022 Open testing Phase</h4>
<div class="row">
<div class="col-md-3">
July 14th 2022,
</div>
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All leaderboards are re-opened
</div>
</div>
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Nov 21st 2022,
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Server Submission will close at 23:59:59 GMT, to prepare for 2023 challenges
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</div>
<br/>
<h4 class="subheading"><s>EPIC-Kitchens 2022 Challenges</s></h4>
<div class="row">
<div class="col-md-3">
<s>Jan 17th 2022,</s>
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All leaderboards are open
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</div>
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<s>June 1st 2022,</s>
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Server Submission Deadline at 23:59:59 GMT
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<s>June 6th 2022,</s>
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Deadline for Submission of Technical Reports on <a href="https://cmt3.research.microsoft.com/EPICCVPR2022">CMT</a> (see guidelines below)
</div>
</div>
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<s>June 19-24 2022,</s>
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Results announced at <a href="https://eyewear-computing.org/EPIC_CVPR22/">10th EPIC@CVPR2022 workshop</a>
</div>
</div>
</div>
</div>
<br/>
<div class="row">
<div class="col-md-12">
<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 2023 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 and <a href="https://forms.office.com/r/u3Hjpvb5Ks">fill this form with your team's details</a>. 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>
</div>
</div>
<!-- <div class="row">
<div class="col-md-12">
<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. To enable your account, <a href="https://forms.office.com/r/u3Hjpvb5Ks">fill this form with your team's details</a>. 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 6th, detailing your entry's technical details.</p>
<p>To <b>submit your technical report</b>, use the <a href="https://cvpr2022.thecvf.com/author-guidelines">CVPR 2022 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/EPICCVPR2022">Link here</a>). Please select the track "EPIC-Kitchens 2022 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:[email protected]">[email protected]</a></p>
</div>
</div>-->
<!-- <div class="row">
<div class="col-md-12">
<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 2022 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 workshop, 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>
<li>
<b>Q.</b> Can I use the large-scale <a href="https://ego4d-data.org/">Ego4D dataset</a> in the EPIC-KITCHENS challenges?<br/>
<b>A.</b> Yes, you can. You need to explicitly declare that in the Supervision Level Scales (SLS) of your submission as follows.
<ul>
<li>When using Ego4D for pretraining, set the Pretraining (PT) flag to level 3 when pretraining in self-supervised manner, and 4 when pretraining with supervision.</li>
<li>When using Ego4D labels as training data, set the Training Data (TD) flag to 4 if using Ego4D solely andd 5 if using Ego4D with other private datasets</li>
</ul>
</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://codalab.lisn.upsaclay.fr/competitions/776#learn_the_details">
<i class="text-center centered fas fa-4x fa-info"></i>
</a>
<br />
<p class="text-muted">Learn about the
<a href="https://codalab.lisn.upsaclay.fr/competitions/776#learn_the_details">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/776#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://codalab.lisn.upsaclay.fr/competitions/776#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://codalab.lisn.upsaclay.fr/competitions/707#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://codalab.lisn.upsaclay.fr/competitions/707#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/707#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://codalab.lisn.upsaclay.fr/competitions/707#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/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://codalab.lisn.upsaclay.fr/competitions/702#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://codalab.lisn.upsaclay.fr/competitions/702#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/702#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://codalab.lisn.upsaclay.fr/competitions/702#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/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://codalab.lisn.upsaclay.fr/competitions/1241#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://codalab.lisn.upsaclay.fr/competitions/1241#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/1241#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://codalab.lisn.upsaclay.fr/competitions/1241#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/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://codalab.lisn.upsaclay.fr/competitions/617#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://codalab.lisn.upsaclay.fr/competitions/617#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/617#results">
<i class="text-center centered fas fa-4x fa-trophy"></i>
</a>
<p class="text-muted">Submit your results on
<a href="https://codalab.lisn.upsaclay.fr/competitions/617#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/aret.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
</div>
</div>
</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="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>
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<a href="https://tobyperrett.github.io">
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<h4>Toby Perrett</h4></a>
<h5>(Apr 2017 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Toby-->
<div class="col-md-3">
<div class="team-member">
<a href="http://iplab.dmi.unict.it/furnari/">
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<h4>Antonino Furnari</h4></a>
<h5>(Jul 2017 - )</h5>
<h6 class="text-muted">University of Catania</h6>
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<div class="col-md-3">
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<a href="https://majian8.github.io/jianma/">
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<h4>Jian Ma</h4></a>
<h5>(Sep 2019 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Jian-->
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<div class="row">
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<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>
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<div class="team-member">
<a href="https://dwhettam.github.io/">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/Dan.jpg" />
<h4>Daniel Whettam</h4></a>
<h5>(July 2020 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
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</div> <!--Dan -->
<div class="col-md-3">
<div class="team-member">
<a href="https://adrianofragomeni.github.io">
<img class="mx-auto rounded-circle" src="{{site.baseurl}}/static/img/profile/Adriano.jpg" />
<h4>Adriano Fragomeni</h4></a>
<h5>(Oct 2020 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Adriano -->
<div class="col-md-3">
<div class="team-member">
<a href="https://binzhubz.github.io/">
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<h4>Bin Zhu</h4></a>
<h5>(Jan 2022 - )</h5>
<h6 class="text-muted">University of Bristol</h6>
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</div> <!--Bin -->
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<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 - 2020)</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
<h6 class="text-muted">(curr.) Nanyang Tech University</h6>
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<div class="col-md-3">
<div class="team-member">
<a href="http://hazeldoughty.github.io">
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<h4>Hazel Doughty</h4></a>
<h5>(Apr 2017 - 2020)</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
<h6 class="text-muted">(curr.) University of Amsterdam</h6>
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</div> <!--Hazel-->
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<a href="https://jonmun.github.io">
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<h4>Jonathan Munro</h4></a>
<h5>(Sep 2017 - 2021)</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
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<a href="http://willprice.org">
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<h4>Will Price</h4></a>
<h5>(Oct 2017 - 2021)</h5>
<h6 class="text-muted">(prev.) University of Bristol</h6>
</div>
</div> <!--Will-->
</div>
<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-uppercase">Research Funding</h2>
<div class="text-muted">
<p> The work on extending EPIC-KITCHENS was supported by the following research grants</p>
<ul class="text-muted">
<li><a href="https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T004991/1">EPSRC Early Career Fellowship UMPIRE</a></li>
<li>University of Bristol Research Fellowship 2018</li>
<li>EPSRC DTP Funding for: Moltisanti, Wray, Doughty, Price, Kazakos and Munro</li>
<li>Piano della Ricerca 2016-2018 linea diIntervento 2 of DMI and by MISE - PON I&C 2014-2020, ENIGMA project (CUP: B61B19000520008) and MIUR AIM - Attrazione e Mobilita Internazionale Linea 1 - AIM1893589 - CUP E64118002540007</li>
<li><a href="https://www.nokia.com/en_int/nokia-technologies">Nokia Technologies</a> - Charitable Donation</li>
<li><a href="http://www.bristol.ac.uk/golding/">Jean Golding Institute, University of Bristol</a> - Seed corn funding</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<section class="bg-light" id="results">
<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-uppercase">Results - 2022 Challenges (June 2022)</h2>
<div class="text-muted">
<div class="row">
<div class="col-md-12">
<h4 class="subheading">EPIC-Kitchens Challenges @CVPR2022, Hybrid CVPR, New Orleans</h4>
<div class="row">
<div class="col-md-3">
Jan 12, 2022
</div>
<div class="col-md-9">
EPIC-Kitchens Challenges 2022 Launched
</div>
</div>
<div class="row">
<div class="col-md-3">
June 1, 2022
</div>
<div class="col-md-9">
Server Submission Deadline at 23:59:59 GMT
</div>
</div>
<div class="row">
<div class="col-md-3">
Jun 4, 2022
</div>
<div class="col-md-9">
Deadline for Submission of Technical Reports
</div>
</div>
<div class="row">
<div class="col-md-3">
June 20, 2022
</div>
<div class="col-md-9">
Results announced at <a href="https://eyewear-computing.org/EPIC_CVPR22/">10th EPIC@CVPR2022</a> Workshop (<a href="https://youtu.be/kLRn-Q48hr0">watch session recording here</a>)
</div>
</div>
<div class="row">
<div class="col-md-3">
July 31, 2022
</div>
<div class="col-md-9">
<a href="./Reports/EPIC-KITCHENS-Challenges-2022-Report.pdf">2022 Challenges Report is now available</a>
</div>
</div>
<!-- <div class="row">
<div class="col-md-3">
July 6, 2021
</div>
<div class="col-md-9">
Technical report for all submissions to the 2021 challenges is now <a href="Reports/EPIC-KITCHENS-Challenges-2021-Report.pdf">available here</a> [Reference <a href="./Reports/2021-bibtex.txt">Bibtex</a>].
</div>
</div> -->
</div>
</div>
<h2 class="section-heading text-uppercase">2022 Challenge Winners</h2>
<div class="col-md">
<img src="{{site.baseurl}}/static/results2022/winnersList1-2022.png" width=100%/>
<img src="{{site.baseurl}}/static/results2022/winnersList2-2022.png" width=100%/>
<img src="{{site.baseurl}}/static/results2022/winnersList3-2022.png" width=100%/>
</div>
<div class="col-md">
<img src="{{site.baseurl}}/static/results2022/winners2022.png" width=100%/>
</div>
<h2 class="section-heading text-uppercase">Action Recognition Challenge - 2022</h2>
<div class="col-md">
<img src="{{site.baseurl}}/static/results2022/AR.png" width=100%/>
</div>