动态比特智能语义变压器对多模式检索进行散列
IEEE transactions on pattern analysis and machine intelligence
|November 7, 2025
概括
这项研究介绍了动态位智语义变压器哈希 (DBSTH) 以实现高效的多模式检索. DBSTH通过将哈希位视为语义概念来增强语义表示,并弥合模式差距.
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:
- 多模组散列将各种数据编码为二进制代码,以实现高效的检索.
- 现有的方法在模态差距,位独立性和捕捉细粒度语义方面扎.
研究的目的:
- 引入一种新的框架,即动态比特智能语义转换器哈希 (Dynamic Bit-wise Semantic Transformer Hashing,DBSTH),以解决当前多模组哈希技术的局限性.
- 提高多模式数据的语义表示能力和检索效率.
主要方法:
- DBSTH将每个哈希位视为用于模式对齐和融合的语义概念.
- 采用动态单元融合策略和变压器编码器来改进概念.
- 包含标签原型学习和掩盖概念学习,以增强概念获取和稳定性.
主要成果:
- 在概念层面上,DBSTH有效地弥合了异质模式的差距.
- 在常规,杂和开放式多模式检索场景中实现卓越的性能.
- 展示了增强的语义表示和位独立性.
结论:
- DBSTH为多式联络哈希和检索提供了强大而有效的解决方案.
- 概念层面的对齐和学习策略显著改善了细粒度的语义关联捕获.
- 该框架显示了在各种条件下高效准确的多媒体检索的前景.
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