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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2105.15203/</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/2105.15203/feed.xml" rel="self" type="application/rss+xml" /><item><title>SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers — mentioned by arketyp</title><link>https://news.ycombinator.com/item?id=29340568</link><guid isPermaLink="false">urn:hackerpapers:mention:2105.15203:29340568</guid><pubDate>Thu, 25 Nov 2021 11:56:23 GMT</pubDate><description>&lt;p&gt;Transformers are exciting as they seem to work on all types of modalities, including vision [1]. It makes me wonder if the transformer module captures some essence of the minicolumn structure found all over the neocortex that Jeff Hawkins raves about, citing Vernon Mountcastle. Hawkins et al talk about grid cells nowadays, location; maybe attention and context is the generalization of such a notion. [1] https://arxiv.org/abs/2105.15203&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2105.15203/"&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>
