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相关概念视频

Retrieval01:12

Retrieval

62
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...
62

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相关实验视频

Updated: May 22, 2025

Cross-Modal Multivariate Pattern Analysis
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Cross-Modal Multivariate Pattern Analysis

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积极监督的跨模态检索

Huaiwen Zhang, Yang Yang, Fan Qi

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    这项研究引入了主动监督交叉模式检索 (ASCMR),以降低标签成本. 新的框架有效地选择信息,公正的多模式数据,以提高检索性能,最小注释.

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    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:

    • 监督交叉模式检索 (SCMR) 在很大程度上依赖于大型,注释的多模式数据集,限制了其实际应用.
    • 现有的SCMR主动学习 (AL) 方法往往忽略了模式间的关系,导致偏见的样本选择,降低了性能.

    研究的目的:

    • 开发一个主动监督跨模式检索 (ASCMR) 框架,有效地识别信息多模式样本,并确保公正的选择.
    • 为了降低SCMR的注释成本,同时保持高的检索性能.

    主要方法:

    • 提出了一个概率的多模式信息性估计,以在统一表示中捕捉模式内和模式间的不确定性.
    • 引入了密度意识的预算分配策略,并对语义密度进行规范化,以实现无偏的样本选择.

    主要成果:

    • 在MS-COCO,NUS-WIDE和MIRFlickr数据集上进行评估,显示了显著的注释成本降低.
    • 仅使用6%,3%和4%的活跃选择样本实现了超过95%的完全监督性能.
    • 在减少注释要求方面表现优于现有的积极学习策略.

    结论:

    • 拟议的ASCMR框架有效地解决了SCMR现有的AL方法的局限性.
    • 由于ASCMR能够实现高检索性能,而标签工作大幅减少,使SCMR变得更加实用.