值得信赖的视觉文本检索
概括
本研究介绍了信任一致的学习 (TCL) 以实现更可靠的视觉文本检索. TCL评估检索不确定性,提高跨模式学习任务的准确性和可解释性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 视觉文本检索将计算机视觉和自然语言处理联系起来.
- 目前的方法缺乏不确定性评估,导致结果不可靠,解释性差.
研究的目的:
- 为可靠的视觉文本检索提出一种新的信任一致学习 (TCL) 框架.
- 在视觉文本检索中引入不确定性评估,以提高可靠性和准确性.
主要方法:
- 使用跨模式相似性来估计不确定性,TCL模型匹配证据.
- 一个一致性模块强制执行双向学习之间的协议,以提高可靠性.
主要成果:
- 在6个基准数据集 (Flickr30K,MS-COCO,MSVD,MSR-VTT,ActivityNet,DiDeMo) 中,TCL展示了优越性和通用性.
- 定性实验验证了TCL框架的可靠性和互操作性.
结论:
- 拟议的TCL框架通过结合不确定性评估来增强视觉文本检索.
- 对于跨模式的检索任务,TCL提供了可靠和可解释的解决方案.
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