<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Poster |</title><link>https://lelain.net/tags/poster/</link><atom:link href="https://lelain.net/tags/poster/index.xml" rel="self" type="application/rss+xml"/><description>Poster</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 14 Jan 2025 00:00:00 +0000</lastBuildDate><image><url>https://lelain.net/media/icon.svg</url><title>Poster</title><link>https://lelain.net/tags/poster/</link></image><item><title>When Foundation Models Meet Astronomical Data</title><link>https://lelain.net/events/nldl2025/</link><pubDate>Tue, 14 Jan 2025 00:00:00 +0000</pubDate><guid>https://lelain.net/events/nldl2025/</guid><description>&lt;p&gt;Poster presented at the Northern Lights Deep Learning Conference 2025 in Tromsø, Norway. This work evaluates the performance of existing visual foundation models (ViT, DINOv2, CLIP) for astronomical data analysis, with a focus on galaxy morphological classification.&lt;/p&gt;</description></item><item><title>When Foundation Models Meet Astronomical Data</title><link>https://lelain.net/events/eas2024/</link><pubDate>Mon, 01 Jul 2024 00:00:00 +0000</pubDate><guid>https://lelain.net/events/eas2024/</guid><description>&lt;p&gt;ePoster presented at the European Astronomical Society Conference 2024 in Padova, Italy, in Session SS10: &lt;em&gt;The impact of the rapidly evolving field of artificial intelligence on astrophysics research: avenues and potential breakthroughs&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;This work evaluates the performance of existing visual foundation models (ViT, CLIP, DINOv2) for astronomical data analysis, with a focus on galaxy morphological classification using GalaxyZoo datasets.&lt;/p&gt;</description></item></channel></rss>