<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>计算机视觉 on s-ai-unix's Blog</title><link>https://s-ai-unix.github.io/tags/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/</link><description>Recent content in 计算机视觉 on s-ai-unix's Blog</description><generator>Hugo -- 0.161.1</generator><language>zh-cn</language><lastBuildDate>Fri, 30 Jan 2026 08:46:42 +0800</lastBuildDate><atom:link href="https://s-ai-unix.github.io/tags/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/index.xml" rel="self" type="application/rss+xml"/><item><title>AI 论文解读系列：Vision Transformer 视觉Transformer</title><link>https://s-ai-unix.github.io/posts/2026-01-30-ai-paper-interpretation-series-vision-transformer-visual-transformer/</link><pubDate>Fri, 30 Jan 2026 08:46:42 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-30-ai-paper-interpretation-series-vision-transformer-visual-transformer/</guid><description>深入解读 Google Research 的 Vision Transformer 论文，从注意力机制的原理出发，剖析图像块嵌入、位置编码、Transformer Encoder 的完整架构，揭示 Transformer 如何在计算机视觉领域挑战 CNN 的统治地位。</description></item></channel></rss>