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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2503.10212/</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/2503.10212/feed.xml" rel="self" type="application/rss+xml" /><item><title>MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis — mentioned by hummuscience</title><link>https://news.ycombinator.com/item?id=43716853</link><guid isPermaLink="false">urn:hackerpapers:mention:2503.10212:43716853</guid><pubDate>Thu, 17 Apr 2025 13:51:41 GMT</pubDate><description>&lt;p&gt;The moment I started reading this, I got reminded of this recent study: https://arxiv.org/html/2503.10212v1 The scope is a bit different. The study uses an LLM to interpret pose estimation data and describe the behavior in each frame. The output is text which can be used to create embeddings of behavior. As someone who works in ethology, that&amp;#x27;s a clever (but maybe expensive) idea. I think the author could use something similar. With multi-person pose estimation models.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2503.10212/"&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>
