HSG：双曲场景图

摘要

场景图表示通过建模物体及其关系实现结构化视觉理解，已广泛应用于多视角和三维场景推理。现有方法如MSG通过对比学习和基于注意力的关联在欧几里得空间中学习场景图嵌入。然而，欧几里得几何无法显式捕捉场景与物体间的层次蕴含关系，限制了所学表征的结构一致性。为此，我们提出双曲场景图（HSG），在双曲空间中学习场景图嵌入，该空间可通过几何距离自然编码层次关系。实验结果表明，HSG在保持强大检索性能的同时提升了层次结构质量，在图级指标上提升最为显著：HSG实现了33.17的PP IoU和最高的33.51 Graph IoU，较最佳AoMSG变体（25.37）提升8.14，彰显了双曲表示学习在场景图建模中的有效性。代码地址：https://github.com/AIGeeksGroup/HSG。

English

Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview and 3D scene reasoning. Existing methods such as MSG learn scene graph embeddings in Euclidean space using contrastive learning and attention based association. However, Euclidean geometry does not explicitly capture hierarchical entailment relationships between places and objects, limiting the structural consistency of learned representations. To address this, we propose Hyperbolic Scene Graph (HSG), which learns scene graph embeddings in hyperbolic space where hierarchical relationships are naturally encoded through geometric distance. Our results show that HSG improves hierarchical structure quality while maintaining strong retrieval performance. The largest gains are observed in graph level metrics: HSG achieves a PP IoU of 33.17 and the highest Graph IoU of 33.51, outperforming the best AoMSG variant (25.37) by 8.14, highlighting the effectiveness of hyperbolic representation learning for scene graph modeling. Code: https://github.com/AIGeeksGroup/HSG.

HSG：双曲场景图

HSG: Hyperbolic Scene Graph

摘要

Support