通过异构双图与类型感知消息传递，提升场景图生成中尾部类别的预测准确率。
Unbiased Scene Graph Generation by Type-Aware Message Passing on Heterogeneous and Dual Graphs
- 构建异构双图并设计选择策略，减少无效边，更好建模物体间交互关系。
- 在两个阶段分别捕捉同类型和跨类型上下文，提升对复杂交互的理解。
- 在两个数据集上均显著提升尾部类别性能，适合关注长尾问题的研究者。
尽管无偏场景图生成已取得显著进展，但头部和尾部类别的预测性能仍受制于一些问题。本文提出一种无偏场景图生成方法（TA-HDG），通过结合异构图与双图结构来建模物体间的交互与非交互关系，并设计主体-客体对选择策略以减少无意义边。此外，引入类型感知消息传递机制，在同类型阶段捕捉关系与物体间的语义上下文，在跨类型阶段分别建模交互与非交互关系的上下文。在两个数据集上的实验表明，该方法在R@K和mR@K指标上均获得提升，验证了其在保持头部类别竞争力的同时，能更准确预测尾部类别。
原文摘要 · Abstract (English)
Although great progress has been made in the research of unbiased scene graph generation, issues still hinder improving the predictive performance of both head and tail classes. An unbiased scene graph generation (TA-HDG) is proposed to address these issues. For modeling interactive and non-interactive relations, the Interactive Graph Construction is proposed to model the dependence of relations on objects by combining heterogeneous and dual graph, when modeling relations between multiple objects. It also implements a subject-object pair selection strategy to reduce meaningless edges. Moreover, the Type-Aware Message Passing enhances the understanding of complex interactions by capturing intra- and inter-type context in the Intra-Type and Inter-Type stages. The Intra-Type stage captures the semantic context of inter-relaitons and inter-objects. On this basis, the Inter-Type stage captures the context between objects and relations for interactive and non-interactive relations, respectively. Experiments on two datasets show that TA-HDG achieves improvements in the metrics of R@K and mR@K, which proves that TA-HDG can accurately predict the tail class while maintaining the competitive performance of the head class.
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