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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rich feature hierarchies for accurate object detection and semantic segmentation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1311.2524/</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/1311.2524/feed.xml" rel="self" type="application/rss+xml" /><item><title>Rich feature hierarchies for accurate object detection and semantic segmentation — mentioned by Animats</title><link>https://news.ycombinator.com/item?id=8622799</link><guid isPermaLink="false">urn:hackerpapers:mention:1311.2524:8622799</guid><pubDate>Tue, 18 Nov 2014 07:17:39 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m enormously impressed, amazed that this works. I&amp;#x27;m reading the papers to understand how they do it. Phase I is segmenting the image into rectangular regions which contain an &amp;quot;object&amp;quot;. That&amp;#x27;s done using the approach in this paper. http://koen.me/research/pub/uijlings-ijcv2013-draft.pdf The regions can overlap, and one region can be completely contained in another. Regions found may turn out to be useless, so the goal is to find too many, rather than too few. That part is reasonably straightforward, and you can see how well it&amp;#x27;s working visually. Phase II is described in this paper…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1311.2524/"&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>
