显式语义引导的双不完整的多模式哈希与标签共发生和标签图的约束
Haoran Zhu1, Xu Lu1, Liang Zhang1
1College of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China.
概括
这项研究介绍了LaDiff-BIMH,这是一种用于多模式散列的新框架,可以有效处理不完整的数据. 它通过解决大型数据集中缺失的功能和标签来提高多媒体检索准确性.
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:
- 多模组散列通过将各种功能集成到紧的二进制代码中来增强大规模的多媒体检索.
- 现有的方法在不完整的多式联运特征和标签方面扎,特别是缺失率很高.
研究的目的:
- 提出LaDiff-BIMH,这是一个针对双不完整多模式哈希的统一框架,它解决了缺失的功能和标签.
- 在数据不完整的情况下,提高多媒体检索的准确性和效率.
主要方法:
- 拉迪夫-BIMH采用了三阶段的方法:标签图局限自编码器用于模态重建,有条件的DDPM用于不完整的模态完成,以及显式语义指导的多模态哈希学习.
- 杆标签共发生和图形约束,用于特征重建和伪标签生成.
- 使用自适应加权融合和歧视性哈希中心来增强哈希代码生成.
主要成果:
- 拉迪夫-BIMH在多模组哈希处理中表现出优越的性能,与最先进的方法相比.
- 该框架有效地处理特征和标签上的双不完整性.
- 提高了生成的哈希代码的语义一致性和可辨别性.
结论:
- 拉迪夫-BIMH为不完整数据的多式联络哈希提供了一个强大的解决方案.
- 拟议的框架在具有挑战性的数据条件下显著推进多媒体检索领域.
- 未来的工作可能会探索处理复杂缺失数据模式的进一步改进.
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