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

Parallel Processing01:20

Parallel Processing

150
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
150

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

Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation.

IEEE transactions on neural networks and learning systems·2025
Same author

A deep-learning-based consistency test approach for Earth system models on HPC systems.

iScience·2025
Same author

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties.

Journal of environmental management·2024
Same author

A Prolonged Artificial Nighttime-light Dataset of China (1984-2020).

Scientific data·2024
Same author

Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction.

Physical review letters·2024
Same author

Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network.

Global change biology·2024

相关实验视频

Updated: Jun 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

520

在高分辨率遥感图像中进行复杂复合物体检测的关系部分意识学习.

Shuai Yuan, Lixian Zhang, Runmin Dong

    IEEE transactions on cybernetics
    |May 20, 2024
    PubMed
    概括

    在高分辨率遥感图像 (RSI) 中检测复杂的复合物体被关系部分意识网络 (REPAN) 改进. 这一框架增强了部分相关性和特征提取,以获得更高的对象检测性能.

    科学领域:

    • 计算机科学 计算机科学
    • 遥感 遥感 遥感 遥感
    • 人工智能的人工智能

    背景情况:

    • 在高分辨率遥感图像 (RSI) 中复杂的复合物体检测是具有挑战性的,原因是离散的部分,可变的布局和模糊的边界.
    • 现有的方法在较弱的相互关系和复合物体 (如发电厂和港口) 的微妙特征方面扎.

    研究的目的:

    • 提出一个端到端的框架,关系部分意识网络 (REPAN),用于在RSI中更好地检测复杂的复合对象.
    • 探索多个对象部分之间的语义相关性和提取区分特征.

    主要方法:

    • 一个部分区域提案网络 (P-RPN) 与蝶单位 (BFU) 定位歧视性地区并减轻特征规模的混.
    • 一个特征关系转换器 (FRT),用于联合部分和全局学习,以捕捉空间关系并增强部分表示.
    • 一个情境检测器 (CD) 使用多关联感知功能来分类和检测部分和整个复合物体.

    主要成果:

    • 拟议的REPAN框架在广泛的实验中始终超越了最先进的方法.
    • 对三组远程传感物体检测数据集进行评估,其中包括四个类别,证明了该方法的有效性.
    • 该方法成功地解决了复合物体检测中可变布局和模糊边界所带来的挑战.

    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
    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    418

    相关实验视频

    Last Updated: Jun 25, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    520
    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
    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    418

    结论:

    • 关系部分感知网络 (REPAN) 是一种高分辨率遥感图像中复杂复合物体检测的有效和优质方法.
    • 该框架能够探索部分之间的语义相关性和空间关系,这大大提高了检测准确性.
    • 雷潘为具有挑战性的遥感物体检测任务提供了强大的解决方案,特别是对于具有复杂结构的物体.