对弱监督视觉接地进行自信意识的伪标签自我纠正
概括
本研究介绍了自信意识的伪标签学习 (CPL) 和CPL++以改善弱监督的视觉接地. 这些方法通过动态验证和纠正可疑链接来增强区域查询关联,优于现有技术.
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:
- 弱监督的视觉接地旨在将文本查询连接到没有直接培训数据的图像区域.
- 现有的方法因区域提案选择的不可靠的跨模式相似性得分而扎过度.
研究的目的:
- 开发一个强大的框架,用于弱监督的视觉接地,克服模型过拟合.
- 提高将文本查询与图像区域关联的准确性和可靠性.
主要方法:
- 拟议的信任意识的伪标签学习 (CPL) 框架使用单模相似性可靠的伪标签生成.
- 引入了使用预先训练的视觉语言模型的跨模式验证模块.
- 开发了基于接地损失的动态验证和自主监督的关联校正模块的CPL ++.
主要成果:
- 五个数据集的实验结果证明了拟议方法的优越性.
- 通过动态验证和纠正可疑的关联,CPL++框架有效地减轻了错误的传播.
- 这些方法在弱监督的视觉接地任务中表现得更好.
结论:
- 拟议的CPL和CPL++框架在弱监督的视觉接地方面提供了显著的改进.
- 动态验证和自我监督的校正是处理不可靠关联的有效策略.
- 该方法通过解决模型过拟合和错误传播来推进视觉接地方面的最新技术.
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