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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Inverse Compositional Spatial Transformer Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1612.03897/</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/1612.03897/feed.xml" rel="self" type="application/rss+xml" /><item><title>Inverse Compositional Spatial Transformer Networks — mentioned by BooglyWoo</title><link>https://news.ycombinator.com/item?id=14217985</link><guid isPermaLink="false">urn:hackerpapers:mention:1612.03897:14217985</guid><pubDate>Fri, 28 Apr 2017 09:18:00 GMT</pubDate><description>&lt;p&gt;&amp;quot;Classical&amp;quot; CV and deep-learning CV needn&amp;#x27;t be opposing one another. There are several cases in which the classical approach is emulated by deep networks - implementing the same carefully thought-out pipelines but in a way that leverages representations learned from huge datasets (which are undeniably very powerful). Some examples are: * Bags of convolutional features for scalable instance search https://arxiv.org/pdf/1604.04653.pdf This paper treats each &amp;#x27;pixel&amp;#x27; of a CNN activation tensor as a local descriptor, clusters them, and describes an image as a bag-of-visual-words histogram. *…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1612.03897/"&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>
