一个因果调整模块用于调整偏差的场景图形生成
概括
这项研究引入了一种新的因果推断方法,以解决Scene Graph Generation (SGG) 中的偏见. 基于调解器的因果链模型和因果调整模块有效地纠正了SGG模型偏差,并改善了零射击关系的识别.
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:
- 现有的场景图形生成 (SGG) 调解方法主要集中在关系分布上,忽略了潜在的对象和对象对分布偏差.
- 这种监督限制了SGG当前退债策略的有效性.
研究的目的:
- 通过因果推理,研究场景图生成 (SGG) 中偏差的根本原因.
- 开发一种新的方法,通过解决倾斜的对象和对象对分布来调整SGG模型.
主要方法:
- 采用因果推理技术来建模对象,对象对和关系分布之间的因果关系.
- 介绍了基于调解器的因果链模型 (MCCM),结合了调解器变量,如同发生分布.
- 提出了因果调整模块 (CAModule),以估计因果结构并生成偏差校正的调整因子.
主要成果:
- 拟议的因果调整模块 (CAModule) 显著提高了SGG中最先进的平均召回率.
- 在零射击关系召回率方面表现出显著的改进.
- 在各种SGG骨干和基准中验证的有效性.
结论:
- 歪曲的对象和对象对分布是SGG偏差的根本原因,超出了关系分布.
- 基于调解器的因果链模型 (MCCM) 和因果调整模块 (CAModule) 提供了基于因果推断的有效方法来处理SGG.
- 拟议的方法提高了SGG的性能,并使零射击关系组成成为可能.
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