Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Nonconscious Mimicry01:13

Nonconscious Mimicry

5.1K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
5.1K
State Space Representation01:27

State Space Representation

515
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
515

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

From Initiation to Recovery: A Longitudinal Analysis of Polymyxin B-Induced Kidney Injury in Clinical Practice.

Drug design, development and therapy·2026
Same author

Early Stopping Without Validation Data in Weakly Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Comparative Prognostic Assessment of EKFC Equations for Mortality Prediction: A Population-based Cohort Study.

Cardiorenal medicine·2026
Same author

Pharmacokinetic/pharmacodynamic target attainment of eravacycline in a liver transplant recipient with refractory VRE <i>faecium</i> infection.

Antimicrobial agents and chemotherapy·2026
Same author

Bibliometric Analysis of White Matter Disease and Novel Drug Therapy.

Current neuropharmacology·2026
Same author

Instance-dependent Early Stopping for Adaptive Data Pruning.

IEEE transactions on pattern analysis and machine intelligence·2026

相关实验视频

Updated: Jul 14, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

NVS-SQA:探索神经合成场景的自我监督质量表示学习,没有参考.

Qiang Qu, Yiran Shen, Xiaoming Chen

    IEEE transactions on pattern analysis and machine intelligence
    |October 30, 2025
    PubMed
    概括

    神经视图合成 (NVS) 质量评估得到了NVS-SQA的改进,NVS-SQA是一种新的无参考方法. 它通过自我监督学习质量表示,优于现有的全参考和无参考方法.

    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机图形 计算机图形
    • 机器学习 机器学习

    背景情况:

    • 神经视图合成 (NVS) 方法,如NeRF和3D高斯分片,可以生成光现实的场景.
    • 目前的质量评估依赖于全参考指标 (PSNR,SSIM,LPIPS),这些指标在NVS中与有限的参考视图作斗争.
    • 获得NVS的人类感知标签具有挑战性,阻碍了数据集创建和模型通用性.

    研究的目的:

    • 开发一个无参考的神经合成场景 (NSS) 质量评估方法 (NVS-SQA).
    • 为了实现无人标签的高质量表示的自我监督学习.
    • 在NSS质量评估的背景下,克服传统自主监督学习的局限性.

    主要方法:

    • 作为学习目标,NVS-SQA使用自我监督,启发式线索和质量评分.
    • 专门的对比对准备过程被用于有效的学习.
    • 该方法避免依赖人类感知标签和广泛的数据集.

    主要成果:

    • 在SRCC,PLCC和KRCC指标中,NVS-SQA显著优于17种无引用方法.
    • 在所有评估指标上的表现方面,NVS-SQA超过了16种全参考方法.
    • 拟议的方法在NSS质量评估中表现出卓越的有效性和效率.

    相关实验视频

    Last Updated: Jul 14, 2026

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    结论:

    • NVS-SQA为无参考NSS质量评估提供了强大而高效的解决方案.
    • 自主监督方法有效地学习质量表示,解决数据稀缺和标签获取挑战.
    • NVS-SQA为评估神经合成场景设定了一个新的基准.