<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VNet on 南巷</title><link>https://nx.wmlab.top/tags/vnet/</link><description>Recent content in VNet on 南巷</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>liuwenhao1968@163.com (南巷)</managingEditor><webMaster>liuwenhao1968@163.com (南巷)</webMaster><copyright>© 2026 南巷</copyright><lastBuildDate>Sat, 10 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://nx.wmlab.top/tags/vnet/index.xml" rel="self" type="application/rss+xml"/><item><title>NxV2Net：裂缝分割科研代码</title><link>https://nx.wmlab.top/portfolio/nxv2net/</link><pubDate>Sat, 10 Jan 2026 00:00:00 +0000</pubDate><author>liuwenhao1968@163.com (南巷)</author><guid>https://nx.wmlab.top/portfolio/nxv2net/</guid><description>面向复杂真实场景的鲁棒裂缝分割开源项目，围绕嵌套多尺度结构、MCFA 融合注意力和 SUES-CRACK 数据集提升泛化能力。</description></item><item><title>V2Net: A Nested Framework for Crack Segmentation Based on Multiscale Feature Learning</title><link>https://nx.wmlab.top/publications/v2net-crack-segmentation/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><author>liuwenhao1968@163.com (南巷)</author><guid>https://nx.wmlab.top/publications/v2net-crack-segmentation/</guid><description>面向工业裂缝分割的嵌套式多尺度特征学习框架，通过 VNet 子结构、匹配式跳跃连接和渐进式特征增强改善尺度变化与细粒度结构表达。</description></item></channel></rss>