外部指导 不完全 交叉模式哈希
概括
本研究引入了外部指导不完整的交叉模式哈希 (EGICH),以提高不完整的多式联运数据的检索准确性. EGICH利用外部知识来重建缺失的信息,在各种场景中优于现有方法.
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:
- 交叉模式哈希 (CMH) 方法假定完整的,配对的多式联络数据,这在现实世界中往往不是这样.
- 现有的不完整的CMH方法由于对分布转移的敏感性和依赖内部数据信号而缺乏模式.
研究的目的:
- 提出一个新的框架,外部指导不完整的交叉模式哈希 (EGICH),以解决现有的不完整的CMH方法的局限性.
- 利用外部知识库来更强大地重建缺失的模式,并减轻跨模式偏差.
主要方法:
- 开发了一个完善与外部指导 (CEG) 模块,以利用外部知识来准确地重建缺失样本的语义.
- 引入了与外部指导 (CLEG) 的一致性学习模块,以使用外部指导的特征将表示与标签语义对齐.
- 实现了一个语义意识的对比哈希 (SCH) 模块,以根据语义相似性改进基于语义相似性的特征分布,以改善歧视.
主要成果:
- EGICH的表现始终和显著地超过了11种最先进的方法.
- 该框架在各种模式缺失场景中表现出强的表现.
- 外部知识整合在增强不完整的跨模式散列方面被证明是有效的.
结论:
- EGICH是第一个将外部知识纳入不完整的跨模式散列的框架.
- 拟议的方法通过利用外部语义信息,有效地处理缺失的模式.
- 在不完整数据的交叉模式检索中,EGICH提供了显著的进步.
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