HSG:双曲场景图(Hyperbolic Scene Graph)
阅读原文· arxiv.org针对现有场景图方法在欧几里得空间难以显式建模层次化关系的问题,研究团队提出Hyperbolic Scene Graph(HSG),将嵌入学习映射至双曲空间,通过几何距离自然编码层次结构。实验显示,HSG在图级别指标上提升显著:Graph IoU达33.51,PP IoU达33.17,较最佳AoMSG变体(25.37)提高8.14,在保持检索性能的同时显著改善层次结构质量。代码已开源。
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.