文本增强图形注意力对交叉模式检索进行哈希
Qiang Zou1, Shuli Cheng1, Anyu Du1
1College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
本研究引入了文本增强图形注意力哈希 (TEGAH) 技术,通过降低深度哈希中的信息来改善交叉模式检索. TEGAH框架提高了大规模媒体数据的检索效率.
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:
- 深度哈希允许高效的存储和检索,这对于不断增长的多式联运数据至关重要.
- 现有的方法与杂的标签和多尺度特征作斗争,增加信息和降低检索精度.
- 文本数据的变化在特征提取过程中增加了信息,阻碍了跨模式检索性能.
研究的目的:
- 开发一个新的框架,文本增强图形注意力哈希 (TEGAH),以实现有效的交叉模式检索.
- 为了最大限度地减少哈希代码和功能生成中的信息.
- 提高大规模媒体数据的检索效率.
主要方法:
- 实施了深度文本特征提取网络和多尺度标签区域融合网络,以减少信息.
- 利用基于Graph-Attention的模式特征融合网络来整合多式联运信息.
- 设计TEGAH以改善不同模式的网络亲和力,并保留语义信息.
主要成果:
- 在TEGAH框架中,跨模式检索性能显著改善.
- 在三个多标签数据集上进行了实验,验证了框架的有效性.
- 泰加超越了现有的最先进的交叉模式哈希方法.
结论:
- TEGAH有效地解决了噪音标签,多尺度特征和跨模式检索中的信息的挑战.
- 拟议的框架增强了特征提取和融合,以获得更好的检索精度.
- TEGAH为高效准确的大规模媒体数据检索提供了一个有前途的解决方案.
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