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Scene Graph Generation (SGG) remains a challenging visual understanding taskdue to its compositional property. Most previous works adopt a bottom-up,two-stage or point-based, one-stage approach, which often suffers from hightime complexity or suboptimal designs. In this work, we propose a novel SGGmethod to address the aforementioned issues, formulating the task as abipartite graph construction problem. To address the issues above, we create atransformer-based end-to-end framework to generate the entity and entity-awarepredicate proposal set, and infer directed edges to form relation triplets.Moreover, we design a graph assembling module to infer the connectivity of thebipartite scene graph based on our entity-aware structure, enabling us togenerate the scene graph in an end-to-end manner. Based on bipartite graphassembling paradigm, we further propose a new technical design to address theefficacy of entity-aware modeling and optimization stability of graphassembling. Equipped with the enhanced entity-aware design, our method achievesoptimal performance and time-complexity. Extensive experimental results showthat our design is able to achieve the state-of-the-art or comparableperformance on three challenging benchmarks, surpassing most of the existingapproaches and enjoying higher efficiency in inference. Code is available:https://github.com/Scarecrow0/SGTR

SGTR+: End-to-end Scene Graph Generation with Transformer