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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1702.00824/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1702.00824/feed.xml" rel="self" type="application/rss+xml" /><item><title>YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video — mentioned by Guyag</title><link>https://news.ycombinator.com/item?id=13586131</link><guid isPermaLink="false">urn:hackerpapers:mention:1702.00824:13586131</guid><pubDate>Tue, 07 Feb 2017 01:27:02 GMT</pubDate><description>&lt;p&gt;Their method is detailed in the whitepaper [1], mainly section 3.2. TL;DR mturk for high level annotations (frame contains/does not contain object in class), and internal annotators for bounding boxes. [1] https://arxiv.org/pdf/1702.00824.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1702.00824/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
