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相关概念视频

Retrieval01:12

Retrieval

179
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
179
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

216
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...
216
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

2
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
2
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

227
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...
227
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.4K
Tip-of-the-Tongue Phenomenon01:10

Tip-of-the-Tongue Phenomenon

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The tip-of-the-tongue (TOT) phenomenon is a cognitive experience characterized by a temporary inability to retrieve specific information from memory despite having a strong feeling of knowing the information. Although individuals cannot access the target word or detail, they frequently recall related elements, such as its initial letter, syllable count, or context. This partial retrieval often causes frustration, as one might recognize a familiar face or know that a name starts with a specific...
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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UCPM:以不确定性为导向的跨模态检索与部分不匹配的对.

Quanxing Zha, Xin Liu, Yiu-Ming Cheung

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |June 6, 2025
    PubMed
    概括

    本研究引入了不确定性引导的与部分不匹配对 (UCPM) 的交叉模式检索,以提高数据检索准确性. UCPM有效地处理不完美匹配的数据,提高跨模式检索任务的性能.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 交叉模式检索 (CMR) 的手动注释是劳动密集型的.
    • 对CMR的互联网数据收集引入了部分不匹配对 (PMP),降低了性能.
    • 目前用于选PMP的现有方法是敏感的,并产生低于最佳的结果.

    研究的目的:

    • 提出一种高效的方法,即以不确定性为导向的与部分不匹配对 (UCPM) 的交叉模式检索,以减轻PMP的影响.
    • 提高处理不完整数据的跨模式检索系统的准确性和稳定性.

    主要方法:

    • 开发了一个以不确定性为导向的分工 (UGD) 策略,以根据不确定性将训练数据分类为干净,杂和硬子集.
    • 实施了不确定性自我校正 (USC) 机制,以稳定和完善培训期间的不确定性估计.
    • 引入了可信度边际损失 (TML),通过调整硬对的边际来提高特征的可区分性.

    主要成果:

    • 在交叉模式检索中,UCPM显著降低了不匹配数据对的不利影响.
    • 对基准数据集的实验表明,与最先进的方法相比,UCPM的有效性和可靠性.
    • 拟议的方法显示了对合成和现实世界PMP的强度的提高.

    更多相关视频

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    结论:

    • 在交叉模式检索中,UCPM为处理部分不匹配对提供了有效的解决方案.
    • 不确定性引导的方法增强了信息对的学习,同时最大限度地减少了不匹配对的噪音.
    • UCPM推进了强大的交叉模式检索的最新技术.