summaryrefslogtreecommitdiff
path: root/site/content/pages/datasets/ijb_c
diff options
context:
space:
mode:
authoradamhrv <adam@ahprojects.com>2019-05-02 19:57:21 +0200
committeradamhrv <adam@ahprojects.com>2019-05-02 19:57:21 +0200
commit98385977e777fa18019d975ad160cc5725e9001d (patch)
tree0d43b1a74be12c12ef1ed261db1f6a7b0ec0f79d /site/content/pages/datasets/ijb_c
parentdcbe971121734dfd1964d151200b4d9db714adba (diff)
fix typos
Diffstat (limited to 'site/content/pages/datasets/ijb_c')
-rw-r--r--site/content/pages/datasets/ijb_c/index.md9
1 files changed, 9 insertions, 0 deletions
diff --git a/site/content/pages/datasets/ijb_c/index.md b/site/content/pages/datasets/ijb_c/index.md
index 46cab323..9e3f1808 100644
--- a/site/content/pages/datasets/ijb_c/index.md
+++ b/site/content/pages/datasets/ijb_c/index.md
@@ -27,6 +27,15 @@ The IARPA Janus Benchmark C is a dataset created by
![caption: A visualization of the IJB-C dataset](assets/ijb_c_montage.jpg)
+## Research notes
+
+From original papers: https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf
+
+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
+
+
+IARPA funds Italian researcher https://www.micc.unifi.it/projects/glaivejanus/
+
{% include 'dashboard.html' %}
{% include 'supplementary_header.html' %}