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| author | adamhrv <adam@ahprojects.com> | 2019-05-02 19:57:21 +0200 |
|---|---|---|
| committer | adamhrv <adam@ahprojects.com> | 2019-05-02 19:57:21 +0200 |
| commit | 98385977e777fa18019d975ad160cc5725e9001d (patch) | |
| tree | 0d43b1a74be12c12ef1ed261db1f6a7b0ec0f79d /site/public/datasets/ijb_c | |
| parent | dcbe971121734dfd1964d151200b4d9db714adba (diff) | |
fix typos
Diffstat (limited to 'site/public/datasets/ijb_c')
| -rw-r--r-- | site/public/datasets/ijb_c/index.html | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/site/public/datasets/ijb_c/index.html b/site/public/datasets/ijb_c/index.html index 3bc23ca5..f58be23f 100644 --- a/site/public/datasets/ijb_c/index.html +++ b/site/public/datasets/ijb_c/index.html @@ -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> |