通过双阶段因果建模生成无偏的场景图形
概括
本研究引入了两阶段因果建模 (TsCM),以解决场景图形生成 (SGG) 中的语义混乱和长尾分布偏差. TsCM通过脱因果干预来改善无偏的SGG性能,以便更好地预测头尾关系.
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:
- 现有的场景图生成 (SGG) 方法主要解决长尾分布偏差.
- 语义混乱,导致对类似关系的错误预测,仍然是SGG的一个被忽视的偏见.
- 噪音数据集引入了未观察到的混因素,限制了标准因果推理技术的有效性,例如Sparse Mechanism Shift (SMS).
研究的目的:
- 开发一种用于Scene Graph Generation (SGG) 的新型调解程序,利用因果推理.
- 为了同时解决长尾分布和语义混偏差.
- 提出一种方法,以保持常见 (头) 类别的性能,同时改善罕见 (尾) 关系的预测.
主要方法:
- 拟议的两阶段因果建模 (TsCM) 来处理SGG中的多个偏差.
- 实施了使用人口损失 (P-Loss) 的因果表示学习阶段,以减轻语义混乱.
- 在第二阶段引入了自适应逻辑调整 (AL-调整),用于对抗长尾分布偏差的因果校准学习.
- 确保TSCM的模型不可知性,以便与各种SGG架构兼容.
主要成果:
- 在受欢迎的SGG基准指标上,TSCM实现了最先进的表现,以平均召回率衡量.
- 该方法显示,相对于现有的退化技术来说,召回率更高.
- TsCM有效地平衡了头尾关系之间的性能,表明了更好的权衡.
结论:
- 拟议的双阶段因果模型 (TsCM) 有效地解决了SGG中的语义混乱和长尾分布偏差.
- TsCM为SGG模型提供了强大的和可适应的退化解决方案.
- 这些发现表明了开发更准确,更公正的场景图生成系统的有希望的方向.
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