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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>July 2024: Challenge Winners announced</li>
<li>Feb 2024: Leaderboards Open for the 2024 Challenge</li>
<li>Jan 2024: EPIC-KITCHENS team has joined the <a href="https://egovis.github.io">EgoVis Board, and will be co-organising a Joint workshop @CVPR in Seattle this June</a></li>
<li>27 June 2023: <a href="#results">Results are publicly announced</a> and leaderboards are open</li>
<li>1 June 2023: Leaderboards are closed</li>
<li>21 May 2023: 10 days to go until leaderboards close. We have now increased number of submission/day to 2.</li>
<li>23 Jan 2023: Leaderboards are now open. Check <a href="#challenges">challenges</a> for details.</li>
<li>23 Jan 2023: <a href="https://epic-kitchens.github.io/epic-sounds">EPIC-SOUNDS dataset now public</a>. ArXiv paper describing the dataset is coming out soon</li>
<li>16 Aug 2022: Announcing <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>3 July 2022: <a href="https://epic-kitchens.github.io/2022.html#results">Results of the 2022 challenges</a> are now public</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">2022 Challenges: <a href="https://epic-kitchens.github.io/2022.html#results">Results</a>, <a href="./Reports/EPIC-KITCHENS-Challenges-2022-Report.pdf">Tech Report</a> [<a href="./Reports/2022-bibtex.txt">Bibtex</a>]</li>
<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/c92b4a3cd3834e9af9666ac82379ff15ca289a83">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 action segment 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>All masks/segmentation annotations are available from <a href="https://epic-kitchens.github.io/VISOR/#downloads">VISOR webpage</a></p>
<p>Audio-only annotations are available from <a href="https://epic-kitchens.github.io/epic-sounds/#downloads">EPIC-SOUNDS webpage</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 2024 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>leaderboards are now open for the <b>challenge phase from Feb 2024</b>.<br/>
<b>In 2024, we have 9 open challenges. These are</b>
<ul>
<li><a href="#vos">Semi-Supervised Video Object Segmentation Challenge</a></li>
<li><a href="#hos">Hand-Object Segmentation Challenge</a></li>
<li><a href="#tracking">TREK-150 Object Tracking Challenge</a></li>
<li><a href="#sounds">EPIC-SOUNDS Audio-Based Interaction Recognition</a></li>
<li><a href="#sounds2">EPIC-SOUNDS Audio-Based Interaction <b>Detection</b> **NEW in 2024** </a></li>
<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">UDA for Action Recognition</a></li>
<li><a href="#challenge-action-retrieval">Multi-Instance Retrieval</a></li>
</ul>
<h4 class="subheading">EPIC-Kitchens 2024 Challenges</h4>
<div class="row">
<div class="col-md-3">
Feb 10th 2024,
</div>
<div class="col-md-9">
All leaderboards are open
</div>
</div>
<div class="row">
<div class="col-md-3">
June 1st 2024,
</div>
<div class="col-md-9">
Server Submission Deadline at 00:00:00 UTC
</div>
</div>
<div class="row">
<div class="col-md-3">
June 6th 2024,
</div>
<div class="col-md-9">
Deadline for Submission of Technical Reports on CMT <a href="https://cmt3.research.microsoft.com/EgoVis2024/">HERE</a>
</div>
</div>
<div class="row">
<div class="col-md-3">
June 17 2024,
</div>
<div class="col-md-9">
Results announced at 1st EgoVis workshop in Seattle <a href="https://egovis.github.io">EPIC@EgoVis@CVPR2024 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 <b>nine</b> 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>This year, we offer <b>four</b> new challenges in: Semi-Supervised Video Object Segmentation using the <a href="http://epic-kitchens.github.io/VISOR/">VISOR</a> annotations, Hand-object-segmentations using the <a href="http://epic-kitchens.github.io/VISOR/">VISOR</a> annotations, single-object tracking and audio-based action recognition using the <a href="https://epic-kitchens.github.io/epic-sounds/">epic-sounds</a> dataset.</p>
<p>To <b>enter any of the nine 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>For the Semi-Supervised VOS challenge, an additional step is required by submitting the model/code using a Docker. Please refer to this challenge's section for more details.</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://cvpr.thecvf.com/Conferences/2024/AuthorGuidelines">CVPR 2024 camera ready author kit (no blind submission)</a>, and submit a report of 2-6 pages inclusive of any references to the EgoVis@CVPR2024 CMT3 website <a href="https://cmt3.research.microsoft.com/EgoVis2024/">Link Here</a>. Please select the track "EPIC-Kitchens 2024 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">
<section class="challenge" id="vos">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Semi-Supervised Video Object Segmentation</h5>
<p class="text-muted">
This challenge uses the newly published <a href="http://epic-kitchens.github.io/VISOR/">VISOR annotations</a>.<br/>
<b>Task. </b>
Given a sub-sequence of frames with M object masks in the first frame, the goal is to segment these through the remaining frames. Other objects not present in the first frame of the sub-sequence are excluded from this benchmark. Note that any of the M objects can be occluded or out of view, and can reappear during the subsequene.
</p>
<p><b>Validation Submission Phase. </b> Between Jan and end of March, interested parties can submit their results on the validation set to the leaderboard. Successful top submissions will be invited to evaluate on the hidden Test Set in the next phase</p>
<p><b>Test Submission (Final Test).</b> Interested participants who have already registered their interest through submitting their models to the Validation Server are now invited to evaluate their models on the test set. As we do not plan to release the first frame annotations for the test set, to avoid overfitting, you are asked to submit the trained models for inference through a Docker that will be evaluated at our end.
We have now announced <a href="https://github.com/epic-kitchens/C6-SemiVOS#instructions-to-evaluate-on-the-test-set"><b>Instructions to evaluate on the test set</b></a>.
You can now contact us at any point to try your model on the test server (<b>Open for submissions until 1st of June</b>). Winners of the challenge are only decided based on the performance on the hidden test set</p>
<b>Evaluation metrics. </b> Average of J-Decay and F-Recall as with other video object segmentation benchmarks.<br/>
<b>Lead:</b> Ahmad DarKhalil (University of Bristol) and Richard Higgins (Univ of Michigan)
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C6-SemiVOS">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C6-SemiVOS">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9767#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/9767#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9767#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/9767#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/VOS.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="hos">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">Hand Object Segmentation</h5>
<p class="text-muted">
<b>Task. </b>
Segment hands and the corresponding objects being interacted with in images.<br/>
<b>Training input. </b> A set of images with hand masks, object masks and hand-object relations. <br/>
<b>Testing input. </b> A set of images in the test set.<br/>
<b>Splits. </b> VISOR Train and validation for training, evaluated on the VISOR test split. <br/>
<b>Evaluation metrics. </b> Mean Average Precision (mAP) @ IOU 0.1 to 0.5.<br/>
<b>Lead:</b> Dandan Shan (University of Michigan), Richard Higgins (University of Michigan) and David Fouhey (University of Michigan)
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C7-VISOR-HoS">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C7-VISOR-HoS">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9969#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/9969#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9969#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/9969#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/HOS.png" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="tracking">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">TREK-150 Object Tracking</h5>
<p class="text-muted">
<b>Task. </b>
The challenge requires to track an object instance through a first-person video sequence. The challenge will be carried out on the <a href="https://machinelearning.uniud.it/datasets/trek150/">TREK-150 dataset</a>, a subset of the EPIC-KITCHENS dataset labeled for single object tracking. More information on the dataset and downloads can be found <a href="https://machinelearning.uniud.it/datasets/trek150/">here</a>.
<br/>
<b>Lead: Matteo Dunnhofer (University of Udine), Antonino Furnari (University of Catania), Giovanni Maria Farinella (University of Catania), Christian Micheloni (University of Udine)</b>
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C8-Object-Tracking">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C8-Object-Tracking">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9597#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/9597#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9597#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/9597#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/tracker.jpg" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="sounds">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">EPIC-SOUNDS: Audio-Based Interaction Recognition</h5>
<p class="text-muted">
<b>Task. </b> Assign an audio-class label to a trimmed segment, indicating the class of interaction taking place in the video. This uses the <a href="http://epic-kitchens.github.io/epic-sounds/">EPIC-SOUNDS</a> dataset annotations. <br/>
<b>Training input. </b> A set of trimmed audio segments, each annotated with one of 44 class labels. <br/>
<b>Testing input. </b> A set of trimmed unlabelled audio segments. <br/>
<b>Splits. </b> Train/Val/Test splits are available
<a href="https://github.com/epic-kitchens/epic-sounds-annotations" target="_blank">here</a>. <br/>
<b>Evaluation metrics. </b> Top-1/5 accuracy for audio class, on the target test set, as well as mAC mAP and mAUC for class-balanced metrics.<br/>
<b>Leads:</b> Jaesung Huh (University of Oxford), Jacob Chalk (University of Bristol), Evangelos Kazkos (ex- Univ of Bristol), Dima Damen (University of Bristol) and Andrew Zisserman (University of Oxford)
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C9-epic-sounds">
<i class="text-center centered fas fa-4x fa-seedling"></i>
</a>
<br />
<p class="text-muted">
<a href="https://github.com/epic-kitchens/C9-epic-sounds">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9729#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/9729#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/9729#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/9729#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Difference from visual and audio annotations is demonstrated in this teaser figure</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/epic-sounds.jpg" style="width: 100%" class="img-responsive"/>
</figure>
</section>
<section class="challenge" id="sounds2">
<div class="row">
<div class="col-md-12">
<h5 class="subheading">EPIC-SOUNDS: Audio-Based Interaction <b>Detection</b></h5>
<p class="text-muted">
<b>Task. </b> Given an untrimmed video, find all temporal segments that indicate an interaction event and assign these a label. This challenge uses the <a href="http://epic-kitchens.github.io/epic-sounds/">EPIC-SOUNDS</a> dataset annotations. <br/>
<b>Training input. </b> A set of untrimmed videos, each video annotated with a number of, potentially overlapping, start-end times and a label out of 44 class labels. <br/>
<b>Testing input. </b> A set of untrimmed videos. <br/>
<b>Splits. </b> Train (shared with recognition) /Val (shared with recognition) /Test (distinct from recognition) splits are available
<a href="https://github.com/epic-kitchens/epic-sounds-annotations" target="_blank">here</a>. <br/>
<b>Evaluation metrics. </b> average mAP, on the target test set<br/>
<b>Leads:</b> Jacob Chalk (University of Bristol), Jaesung Huh (University of Oxford), Evangelos Kazkos (ex- Univ of Bristol), Dima Damen (University of Bristol) and Andrew Zisserman (University of Oxford)
</p>
</div>
</div>
<div class="details row text-center">
<div class="col-md">
<a href="https://github.com/epic-kitchens/C10-epic-sounds-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/C10-epic-sounds-detection">Get started</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/17921#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/17921#learn_the_details-submission-format">submission format details</a>
</p>
</div>
<div class="col-md">
<a href="https://codalab.lisn.upsaclay.fr/competitions/17921#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/17921#results">CodaLab website</a>
</p>
</div>
</div>
<figure class="text-muted">
<figcaption>Difference from visual and audio annotations is demonstrated in this teaser figure</figcaption>
<img src="{{ site.baseurl }}/static/img/challenges-epic-100/epic-sounds-detection.jpg" style="width: 100%" class="img-responsive"/>
</figure>
</section>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="subheading">Original 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>
</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 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">