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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Detection of Black Holes01:10

Detection of Black Holes

2.2K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.2K
Modeling and Similitude01:12

Modeling and Similitude

288
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
288
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

421
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
421
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.5K

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

RoLiC: A Robust LiDAR-Camera Fusion Framework for 3D Object Detection.

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

The effect of customer incivility on proactive customer service performance mediated by emotional exhaustion and moderated by proactive personality.

Scientific reports·2026
Same author

Sparse Variational Information Bottleneck Gaussian Processes for Uncertainty Estimation.

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

Robust Trusted Conflictive Multiview Collaborative Contrastive Learning.

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

VB-Adapter: Variational Bayesian Adapter for Cross-Domain Speech Representation Learning.

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

Taming vision transformers for clinical laryngoscopy assessment.

Journal of biomedical informatics·2025

相关实验视频

Updated: Jul 17, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K

通过相似性传播进行零射击的人物对象交互检测.

Daoming Zong, Shiliang Sun

    IEEE transactions on neural networks and learning systems
    |September 6, 2023
    PubMed
    概括

    这项研究解决了对看不见的物体的人与物体交互 (HOI) 检测. 一个新的相似性传播方案和伪监督提高了基于变压器的HOI检测准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 人物对象交互 (HOI) 检测对于理解复杂场景至关重要.
    • 目前基于变压器的方法在检测涉及未见物体的HOI方面遇到了困难.
    • 性能降低源于将新奇的对象错误地归类为熟悉的对象.

    研究的目的:

    • 改进基于变压器的人物对象交互检测,以检测看不见的物体.
    • 为应对被误认为新奇物体的可混可见物体的挑战.
    • 增强模型对新交互类别的概括能力.

    主要方法:

    • 提出了一个相似性传播 (SP) 方案,使用等号相似性来调节预测边缘.
    • 引入了对看不见的对象的伪监督,利用类语义相似性.
    • 嵌入语义意识的实例级和交互级对比损失与变压器架构.
    • 增强的视觉表示,以更好地实现类内紧性和类间分离性.

    主要成果:

    • 拟议的SP计划有效地减少了看不见物体的错误分类.
    • 伪监督对新型对象类别的学习表示有助于学习.
    • 语义意识的对比损失显著改善了视觉特征的歧视.

    更多相关视频

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K
    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

    568

    相关实验视频

    Last Updated: Jul 17, 2025

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.6K
    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

    568
  • 该模型在零射击设置中在V-COCO和HICO-DET基准测试中实现了最先进的性能.
  • 结论:

    • 开发的方法成功地提高了零射击HOI检测能力.
    • 这种方法可以减轻由看不见的物体引起的性能下降.
    • 这项工作提供了一个强大的框架,可以将HOI检测推广到新的场景中.