双阶段框架与软标签蒸和空间提示图像-文本检索的提示
Ran Jin1,2, Zhengang Li1, Fang Deng1
1Zhejiang Wanli University, School of Big Data and Software Engineering, Ningbo, China.
PloS one
|October 10, 2025
概括
本研究引入了一种双阶段的培训框架,通过使用软标记蒸 (SLD) 和空间文本提示 (STP) 来解决模式间匹配和细粒度本地化缺陷来提高图像文本检索准确度. 这种新的方法增强了交叉模式的理解,以获得更好的检索性能.
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:
- 视觉语言预训练 (VLP) 已经推进了跨模式的任务.
- 目前的图像-文本检索方法存在跨模式匹配和细粒度本地化缺陷,限制了准确性.
研究的目的:
- 提出一种新的双阶段培训框架,以克服图像文本检索方面的局限性.
- 为了提高跨模式检索任务的准确性和细粒度对齐.
主要方法:
- 实施了双阶段培训框架.
- 在第一阶段采用软标签蒸 (SLD) 来调整图像-文本对比关系并减轻过度匹配.
- 在第二阶段引入了空间文本提示符 (STP),以改善视觉接地和细粒度对齐.
主要成果:
- 拟议的方法显著优于标准图像文本检索数据集的最先进方法.
- 通过增强对齐,在跨模式检索任务中证明了更高的准确性.
结论:
- 双阶段培训框架有效地解决了图像文本检索的关键挑战.
- 结合SLD和STP,在交叉模式的理解和检索方面实现了卓越的性能.
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