From 98385977e777fa18019d975ad160cc5725e9001d Mon Sep 17 00:00:00 2001
From: adamhrv <adam@ahprojects.com>
Date: Thu, 2 May 2019 19:57:21 +0200
Subject: fix typos

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 site/public/datasets/ijb_c/index.html | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'site/public/datasets/ijb_c')

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@@ -75,7 +75,11 @@
     <div><a href='https://www.nist.gov/programs-projects/face-challenges' target='_blank' rel='nofollow noopener'>nist.gov</a></div>
   </div></div><p>[ page under development ]</p>
 <p>The IARPA Janus Benchmark C is a dataset created by</p>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/ijb_c_montage.jpg' alt=' A visualization of the IJB-C dataset'><div class='caption'> A visualization of the IJB-C dataset</div></div></section><section>
+</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/ijb_c_montage.jpg' alt=' A visualization of the IJB-C dataset'><div class='caption'> A visualization of the IJB-C dataset</div></div></section><section><h2>Research notes</h2>
+<p>From original papers: <a href="https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf">https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf</a></p>
+<p>Collection for the dataset began by identifying CreativeCommons subject videos, which are often more scarce thanCreative Commons subject images.   Search terms that re-sulted in large quantities of person-centric videos (e.g. “in-terview”) were generated and translated into numerous lan-guages including Arabic, Korean, Swahili, and Hindi to in-crease diversity of the subject pool. Certain YouTube userswho upload well-labeled, person-centric videos, such as the World  Economic  Forum  and  the  International  University Sports Federation were also identified. Titles of videos per-taining to these search terms and usernames were scrapedusing the YouTube Data API and translated into English us-ing the Yandex Translate API4. Pattern matching was per-formed to extract potential names of subjects from the trans-lated titles, and these names were searched using the Wiki-data  API  to  verify  the  subject’s  existence  and  status  as  a public figure,  and to check for Wikimedia Commons im-agery.  Age, gender, and geographic region were collectedusing the Wikipedia API.Using the candidate subject names, Creative Commonsimages  were  scraped  from  Google  and  Wikimedia  Com-mons,  and  Creative  Commons  videos  were  scraped  fromYouTube. After images and videos of the candidate subjectwere  identified,  AMT  Workers  were  tasked  with  validat-ing the subject’s presence throughout the video.  The AMTWorkers marked segments of the video in which the subjectwas present, and key frames</p>
+<p>IARPA funds Italian researcher <a href="https://www.micc.unifi.it/projects/glaivejanus/">https://www.micc.unifi.it/projects/glaivejanus/</a></p>
+</section><section>
   <h3>Who used IJB-C?</h3>
 
   <p>
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