交叉模式引导视觉表示学习用于社交图像检索
概括
本研究介绍了交叉模式指导视觉表示 (CGVR),通过过噪音标签来改善社交图像检索. CGVR增强了图像表示学习,以获得更好的搜索准确性.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 机器学习 机器学习
背景情况:
- 社交图像拥有来自用户贡献的丰富但杂的标签,阻碍了有效的图像检索.
- 现有的交叉模式学习方法与孤立的,杂的标签和单模式检索要求作斗争.
研究的目的:
- 开发一个框架,使用噪音标签精确的单模社会图像检索.
- 为了有效地过噪音,并利用交叉模式的相关性,以实现准确的图像表示学习.
主要方法:
- 拟议的交叉模式指导视觉表示 (CGVR) 框架.
- 从噪音标签中提取准确的语义图像表示.
- 将表示学习能力转移到仅用于图像的散列子网络.
- 在注意力中嵌入常识标签关系,以获得共同的上下文意识.
主要成果:
- 与最先进的方法相比,CGVR在平均平均精度 (MAP) 中取得了显著的改进.
- 在社交形象基准上表现出大约8.82和5.45点的MAP改进.
- 建立了CGVR作为社交图像检索的新基准.
结论:
- CGVR有效地解决了社交图像检索中噪音标签的挑战.
- 该框架成功地通过利用跨模式交互来学习精确的视觉表示.
- CGVR提供了一个强大的解决方案,用于从杂的社交图像数据中单模式检索.
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