<?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>Dense-Features on Xu'Blog</title><link>https://xuquant.com/tags/dense-features/</link><description>Recent content in Dense-Features on Xu'Blog</description><image><title>Xu'Blog</title><url>https://xuquant.com/og-default.png</url><link>https://xuquant.com/og-default.png</link></image><generator>Hugo -- 0.152.2</generator><language>zh</language><lastBuildDate>Sat, 24 Jan 2026 10:00:00 +0800</lastBuildDate><atom:link href="https://xuquant.com/tags/dense-features/index.xml" rel="self" type="application/rss+xml"/><item><title>DINOv3：自监督视觉基模的规模化困局与 Gram Anchoring 破局</title><link>https://xuquant.com/posts/world-models/dinov3/</link><pubDate>Sat, 24 Jan 2026 10:00:00 +0800</pubDate><guid>https://xuquant.com/posts/world-models/dinov3/</guid><description>DINOv3 核心贡献剖析：Gram anchoring 如何解决大规模自监督训练中 dense feature 退化的根本问题，7B 参数 SSL 模型的训练工程，以及它在深度估计和 3D 匹配上的突破意味着什么。</description></item><item><title>V-JEPA 2.1: When Self-Supervised Vision Learns to See Every Pixel</title><link>https://xuquant.com/posts/world-models/vjepa-2.1/</link><pubDate>Sat, 10 Jan 2026 10:00:00 +0800</pubDate><guid>https://xuquant.com/posts/world-models/vjepa-2.1/</guid><description>A deep analysis of V-JEPA 2.1&amp;#39;s architectural innovations — dense predictive loss, deep self-supervision, multi-modal tokenizer, and scaling — tracing the path from collapsed context tokens to dense features that encode spatial structure, and the connection to depth estimation as geometric grounding.</description></item></channel></rss>