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

Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

159
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
159
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

160
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
160
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Prosopagnosia01:24

Prosopagnosia

206
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
206
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.2K
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.2K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.2K

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

Bridging Subjectivity in Affective Explanation Captioning via Consensus-Prompted Emotion Reasoning.

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

Looking Broader for Knowledge Distillation Via Receptive-Field Alignment.

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

Chemistry-Informed Machine Learning Framework for Predicting Structural Properties in Osmabenzene Complexes.

The journal of physical chemistry letters·2026
Same author

Parallel Diffusion Solver via Residual Dirichlet Policy Optimization.

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

Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns.

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

Advancing In-Context Learning for Efficient and Stable Medical Report Generation.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 17, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

472

协作对比精细化用于弱监督的人搜索搜索

Chengyou Jia, Minnan Luo, Caixia Yan

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 29, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一个新的协作对比精制 (CCR) 框架,用于弱监督人员的搜索. CCR提高了伪标签准确性和样本学习,优于现有方法并匹配监督方法.

    更多相关视频

    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
    05:48

    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

    Published on: August 9, 2024

    1.5K
    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K

    相关实验视频

    Last Updated: Jul 17, 2025

    Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
    09:09

    Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

    Published on: September 27, 2024

    472
    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
    05:48

    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

    Published on: August 9, 2024

    1.5K
    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K

    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:

    • 弱监督的人搜索列车模型只使用边界框注释,缺乏身份标签.
    • 聚类算法经常产生不准确的伪标签,并遭受不平衡的身份分布,导致显著的噪音.
    • 现有的方法与标签噪声和不平衡的数据作斗争,阻碍了在人身搜索任务中的执行.

    研究的目的:

    • 提出一个新的协作对比精制 (CCR) 框架,用于弱监督人员的搜索.
    • 通过使用先进的对比策略,共同完善伪标签和样本学习过程.
    • 为了应对在人搜索中不准确的伪标签和不平衡的身份分布的挑战.

    主要方法:

    • 开发了一个协作对比精制 (CCR) 框架,利用混合对比策略.
    • 采用混合的对比策略,将视觉和上下文线索结合起来,用于伪标签的改进.
    • 实施了采样和噪声对比策略,通过区分正和噪声样本来减轻不平衡数据的影响.

    主要成果:

    • 通过混合相似性探索,CCR框架在改进伪标签方面表现出卓越的表现.
    • 该方法有效地区分了查询和噪声样本,增强了样本学习过程.
    • 与最先进的弱监管方法相比,CUHK-SYSU数据集实现了3%以上的mAP改进.

    结论:

    • 拟议的CCR框架通过提高伪标签质量和样本学习,显著增强弱监督人员的搜索.
    • CCR 取得了最先进的结果,超越了现有的弱监管方法和竞争对手的监管方法.
    • 该框架利用多样化的未标记数据的能力为未来的人身搜索研究提供了有希望的方向.