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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning a Unified Embedding for Visual Search at Pinterest · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1908.01707/</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/1908.01707/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning a Unified Embedding for Visual Search at Pinterest — mentioned by esafak</title><link>https://news.ycombinator.com/item?id=35832872</link><guid isPermaLink="false">urn:hackerpapers:mention:1908.01707:35832872</guid><pubDate>Fri, 05 May 2023 17:58:30 GMT</pubDate><description>&lt;p&gt;These are called binary embeddings, and they have been used successfully at pinterest (https://www.arxiv-vanity.com/papers/1908.01707/) and Tencent (https://paperswithcode.com/paper/binary-embedding-based-retr...) I can&amp;#x27;t speak for the competition, but weaviate seems to support them: https://weaviate.io/developers/weaviate/concepts/binary-pass...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1908.01707/"&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>
