HSG: Hyperbolic Scene Graph

TL;DR

HSG introduces a hyperbolic space-based scene graph embedding method that better captures hierarchical relationships, improving structural quality and graph metrics in visual scene understanding.

Contribution

The paper presents a novel hyperbolic embedding approach for scene graphs, enhancing hierarchical structure modeling over traditional Euclidean methods.

Findings

HSG achieves higher PP IoU and Graph IoU scores than Euclidean baselines.

Hyperbolic embeddings improve hierarchical structure quality.

Code is available at https://github.com/AIGeeksGroup/HSG.

Abstract

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.