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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>High-Resolution Representations for Labeling Pixels and Regions · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1904.04514/</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/1904.04514/feed.xml" rel="self" type="application/rss+xml" /><item><title>High-Resolution Representations for Labeling Pixels and Regions — HN submission</title><link>https://news.ycombinator.com/item?id=20584883</link><guid isPermaLink="false">urn:hackerpapers:submission:1904.04514:20584883</guid><pubDate>Thu, 01 Aug 2019 15:48:04 GMT</pubDate><description>&lt;p&gt;HRNet; the most powerful new vision CNN architecture to come out since AlexNet!&lt;/p&gt;&lt;p&gt;https://www.youtube.com/watch?v=sIP3MrFWCpg Join JingDong Wang - a Principal Research Manager from MSR Asia on a discussion of HRNet; the most exciting and powerful new vision CNN architecture to come out since AlexNet! This is seriously impressive and makes me incredibly proud to work for Microsoft. Just like in 2015 when we produced the best-of-breed vision architecture RESNET – this takes it to a whole new level. HRNET is home-grown at Microsoft and has already demonstrated state-of-the-art performance in image classification, semantic segmentation, object detection, facial landmark…&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1904.04514/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
