通过因果特征增强网络探索场景图生成关系的本质
概括
这项研究引入了一个因果特征增强网络 (CFEN),通过关注对象交互而不是统计相关性来改进场景图表生成 (SGG). 通过分析因果特征和对象特定信息,CFEN增强了关系识别.
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:
- 场景图形生成 (SGG) 对于视觉感知和推理至关重要,表示对象及其关系.
- 当前的SGG方法通常依赖于语言先验或统计知识,这些知识可能不会准确地反映语义相互作用.
- 在捕捉关系的真实语义本质方面存在一个差距,这应该源自对象交互.
研究的目的:
- 提出一种新的因果特征增强网络 (CFEN),以更准确地生成场景图.
- 挖掘对象和关系之间的基本语义特征,超越统计依赖.
- 增强对象交互的理解,以便在SGG中更好地识别关系.
主要方法:
- 将对象特征分解为类通用和对象特定的组件.
- 使用因果图框架来分析现有的SGG方法并测量对象特定特征的影响.
- 实施反事实训练框架,以计算事实和反事实逻辑之间的差异.
- 引入使用KL分歧的分配匹配损失,以调节基于反事实输出的关系预测.
主要成果:
- 拟议的CFEN有效地挖掘了关系识别的基本语义特征.
- 反事实培训框架成功地测量了对象特定特征的影响.
- 在VG150和VrR-VG数据集上的实验结果证明了CFEN在最先进的方法上的优越性.
结论:
- 通过专注于因果语义特征,CFEN在场景图形生成方面取得了重大进展.
- 与传统的SGG方法相比,该方法提供了对象相互作用的更准确的表示.
- 在已建立的数据集上,CFEN建立了一个新的SGG性能基准.
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