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Updated: Mar 11, 2026

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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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为了跨模式的检索,Aleatoric-Epistemic联合不确定性建模
IEEE transactions on cybernetics
|March 9, 2026
概括
本研究引入了一种跨模式检索的新框架,该框架模拟数据和模型不确定性. 通过共同估计 aleatoric 不确定性和 epistemic 不确定性,该方法提高了视觉语言任务的检索准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 跨模式检索利用了像CLIP这样的大规模视觉语言预训练模型.
- 现有的方法将模式映射到共享空间,但与数据和模型不确定性作斗争,导致不可靠的结果.
- 不确定性源于数据模糊性和噪音数据模型对.
研究的目的:
- 为可靠的不确定性建模提出一个新的跨模式检索框架,AEUM.
- 为了解决数据 (随机不确定性) 和模型 (系统不确定性) 的不确定性.
- 通过纠正初始相似性得分来提高跨模式检索的准确性.
主要方法:
- 在AEUM中,AEUM联合建模了 aleatoric 不确定性 (AU) 和 epistemic 不确定性 (EU).
- 用每种模式的可学习语义令牌来估计AU,以衡量数据诱导的不确定性.
- 通过证据学习来估计EU,以提高对噪音数据的稳定性.
主要成果:
- 拟议的AEUM框架证明了在多个基准中有效性和概括性.
- 在七个数据集上进行了实验:五个视频文本 (MSRVTT,LSMDC,MSVD,VATEX,DiDeMo) 和两个图像文本 (MSCOCO,Flickr30K).
- 该方法通过考虑不确定性,显著提高了检索准确性.
结论:
- AEUM提供可靠的不确定性估计,以改善跨模式检索.
- 对 aleatoric 和 epistemic 不确定性的联合建模导致更准确和更强大的检索结果.
- 该框架在各种视频文本和图像文本检索任务上表现出强的表现.
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