基于双重聚合的联合模式相似性散列用于跨模式检索
概括
本研究介绍了基于双聚合的联合模式相似性哈希 (DAJSH) 以实现高效的跨模式检索. DAJSH增强了语义对齐和数据关系,改善了哈希性能,而不需要标签.
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:
- 跨模式散列将多模式数据映射到一个统一的空间,以便有效检索.
- 无监督的方法是有价值的,因为它们不需要外部标签信息.
- 现有的方法在语义对齐和构建可靠的亲和矩阵方面面临挑战.
研究的目的:
- 提出一种新的无监督的交叉模式哈希方法,即基于双聚合的联合模式相似性哈希 (DAJSH).
- 解决无监督交叉模式散列中的语义对齐和内在数据关系挑战.
- 提高跨模式检索的准确性和效率.
主要方法:
- 使用变压器编码器将图像和文本功能融合起来,以实现增强的语义对齐.
- 采用对比损失来优化跨模式一致性.
- 开发一个双聚合亲和矩阵构造方案,整合模式内和模式间的相似性.
主要成果:
- 与最先进的方法相比,DAJSH表现出了显著的性能改善.
- 在MIR Flickr上,性能增长范围为1.9%至5.1%,在NUS-WIDE上为0.9%至5.8%,在MS COCO上为0.6%至2.6%.
- 拟议的双重聚合方案有效地保留了跨模式的语义信息.
结论:
- DAJSH有效地提高语义对齐,并捕获内在数据关系.
- 该方法为学习哈希代码提供了更可靠的亲和矩阵.
- DAJSH代表了无监督的跨模式哈希和检索的重大进步.
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