<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Feature-Pyramid-Networks on 111qqz's blog</title><link>https://111qqz.com/en/tags/feature-pyramid-networks/</link><description>Recent content in Feature-Pyramid-Networks on 111qqz's blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2015-2026 111qqz</copyright><lastBuildDate>Sun, 08 Dec 2019 17:30:50 +0800</lastBuildDate><atom:link href="https://111qqz.com/en/tags/feature-pyramid-networks/index.xml" rel="self" type="application/rss+xml"/><item><title>FPN:Feature Pyramid Networks 学习笔记</title><link>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2019-12-08-feature-pyramid-networks/</link><pubDate>Sun, 08 Dec 2019 17:30:50 +0800</pubDate><guid>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2019-12-08-feature-pyramid-networks/</guid><description>&lt;p&gt;检测不同尺度的物体一直是计算机视觉领域中比较有挑战性的事情．我们先从之前的工作出发，并对比FPN比起之前的工作有哪些改进．&lt;/p&gt;</description></item></channel></rss>