Generating scene graph to describe all the relations inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. To improve the efficiency of scene graph generation, we propose a subgraph-based connection graph to concisely represent the scene graph during the inference. A bottom-up clustering method is first used to factorize the entire scene graph into subgraphs, where each subgraph contains several objects and a subset of their relationships. By replacing the numerous relationship representations of the scene graph with fewer subgraph and object features, the computation in the intermediate stage is significantly reduced. In addition, spatial information is maintained by the subgraph features, which is leveraged by our proposed Spatial-weighted Message Passing~(SMP) structure and Spatial-sensitive Relation Inference~(SRI) module to facilitate the relationship recognition. On the recent Visual Relationship Detection and Visual Genome datasets, our method outperforms the state-of-the-art method in both accuracy and speed.

本文提出了基于子图连接图的场景图生成方法，并采用自下而上的聚类方法将整个场景图分解为子图，并用更少的子图和对象特征替换场景图的众多关系表示，以显著降低中间阶段的计算。采用空间加权消息传递和空间敏感关系推理模块，维护空间信息以促进关系识别，该方法在视觉关系检测和视觉基因组数据集中均优于现有最先进的方法。

可分解网络：一种基于子图的场景图生成高效框架