Scene Graph Generation (SGG) remains a challenging visual understanding task
due to its compositional property. Most previous works adopt a bottom-up,
two-stage or point-based, one-stage approach, which often suffers from high
time complexity or suboptimal designs. In this work, we propose a novel SGG
method to address the aforementioned issues, formulating the task as a
bipartite graph construction problem. To address the issues above, we create a
transformer-based end-to-end framework to generate the entity and entity-aware
predicate proposal set, and infer directed edges to form relation triplets.
Moreover, we design a graph assembling module to infer the connectivity of the
bipartite scene graph based on our entity-aware structure, enabling us to
generate the scene graph in an end-to-end manner. Based on bipartite graph
assembling paradigm, we further propose a new technical design to address the
efficacy of entity-aware modeling and optimization stability of graph
assembling. Equipped with the enhanced entity-aware design, our method achieves
optimal performance and time-complexity. Extensive experimental results show
that our design is able to achieve the state-of-the-art or comparable
performance on three challenging benchmarks, surpassing most of the existing
approaches and enjoying higher efficiency in inference. Code is available:
this https URL

通过使用双分图构建方法和终到终的框架，我们提出了一种新的场景图生成方法，有效地解决了组合属性所带来的挑战。实验证明，我们的方法在三个具有挑战性的基准测试中展现出了优秀的表现和高效率的推理能力，超过了现有方法的大部分，达到了最佳性能和时间复杂度。