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

Deconvolution01:20

Deconvolution

186
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
186
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Downsampling01:20

Downsampling

183
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
183

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

OmniCharacter++: Towards Comprehensive Benchmark for Realistic Role-Playing Agents.

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

Sharpness-Aware Fine-Tuning for OOD Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Stable-Hair V2: Real-World Hair Transfer via Multiple-View Diffusion Model.

IEEE transactions on visualization and computer graphics·2026
Same author

Vocabulary-Free Image Classification and Semantic Segmentation.

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

High-Resolution Open-Vocabulary Object 6D Pose Estimation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models.

IEEE transactions on pattern analysis and machine intelligence·2025

相关实验视频

Updated: Jul 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

在点云细分中用于域调整的组成语义混合.

Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni

    IEEE transactions on pattern analysis and machine intelligence
    |August 30, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了构成性语义混合,这是一种用于3D点云语义细分的新型无监督域适应技术. 它通过混合数据样本来增强跨不同传感器和环境的模型概括性.

    更多相关视频

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.6K

    相关实验视频

    Last Updated: Jul 17, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    24.6K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D数据处理 3D数据处理

    背景情况:

    • 对于3D点云语义细分的深度学习模型,由于域转移 (传感器或环境的差异) 导致的概括性困难.
    • 现有的域调整方法通常需要特定的数据格式 (例如,范围视图地图) 或多模输入,从而限制了它们的适用性.
    • 使用样本混合的图像域适应技术通过操纵输入数据提供了更灵活的方法.

    研究的目的:

    • 引入第一个无监督域适应技术,用于基于语义和几何样本混合的3D点云语义细分.
    • 开发一种方法,以提高深度学习模型的概括能力,以便在不同领域进行点云细分.
    • 在无监督和半监督环境中评估拟议方法的有效性.

    主要方法:

    • 建议在3D点云语义细分中进行无监督域调整的"构成语义混合".
    • 引入了一个双分支对称网络架构,同时处理源域和目标域点云.
    • 每个网络分支中的两个域的集成数据片段和语义信息 (源标签,目标伪标签).
    • 纳入可选的半监督学习,使用有限的人类点级注释.

    主要成果:

    • 拟议的组合语义混合显著优于无监督域适应点云细分的最先进方法.
    • 该方法还在半监督的设置中表现出卓越的性能,进一步提高了对有限注释的准确性.
    • 在合成到真实和真实到真实域调整场景中,对LiDAR数据集进行了评估.

    结论:

    • 组合语义混合是一种有效的无监督域调整技术,用于3D点云语义细分.
    • 双分支网络架构成功地利用跨域信息,以改善泛化.
    • 该方法提供了一种灵活而强大的解决方案,用于将模型适应各种现实世界的条件.