一个反向因果框架,以减轻虚假的相关性,以消除偏差的场景图形生成
概括
本研究引入了一种新的场景图生成 (SGG) 反向因果框架,以解决现有方法中的偏差. 新的框架,RcSGG,减轻了虚假的相关性,并提高了预测的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的两阶段场景图生成 (SGG) 框架使用因果链结构.
- 这种结构可能会导致虚假的相关性和偏见,例如尾巴关系被预测为头部关系.
研究的目的:
- 提出一个新的框架,RcSGG,重建因果结构以减轻SGG的偏差.
- 为了提高场景图表生成的准确性和可靠性.
主要方法:
- 将因果链重建为反向因果结构,将分类器输入作为混器.
- 使用主动反向估计 (ARE) 来估计反向因果关系.
- 使用最大信息采样 (MIS) 来提高反向因果关系估计.
主要成果:
- 拟议的RcSGG框架理论上减轻了虚假的相关性和诱导的偏见.
- 综合性实验表明,在受欢迎的基准指标上,最先进的平均召回率.
- 该框架显示了各种SGG方法的有效性.
结论:
- 对于传统的SGG框架中固有的偏见,RcSGG提供了一个强大的解决方案.
- 反向因果结构有效地解决了标准因果链的局限性.
- 这项工作推动了场景图形生成领域的发展,并提高了性能.
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