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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ferret: Refer and Ground Anything Anywhere at Any Granularity · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.07704/</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/2310.07704/feed.xml" rel="self" type="application/rss+xml" /><item><title>Ferret: Refer and Ground Anything Anywhere at Any Granularity — mentioned by CaptainOfCoit</title><link>https://news.ycombinator.com/item?id=38745720</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.07704:38745720</guid><pubDate>Sat, 23 Dec 2023 17:01:57 GMT</pubDate><description>&lt;p&gt;Maybe the abstract of the paper is a better introduction to what this is: &amp;gt; We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding open-vocabulary descriptions. To unify referring and grounding in the LLM paradigm, Ferret employs a novel and powerful hybrid region representation that integrates discrete coordinates and continuous features jointly to represent a region in the image. To extract the continuous features of versatile regions, we propose a spatial-aware visual…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.07704/"&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>
