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This work presents an enhanced approach to generating scene graphs byincorporating a relationship hierarchy and commonsense knowledge. Specifically,we propose a Bayesian classification head that exploits an informativehierarchical structure. It jointly predicts the super-category or type ofrelationship between the two objects, along with the detailed relationshipunder each super-category. We design a commonsense validation pipeline thatuses a large language model to critique the results from the scene graphprediction system and then use that feedback to enhance the model performance.The system requires no external large language model assistance at test time,making it more convenient for practical applications. Experiments on the VisualGenome and the OpenImage V6 datasets demonstrate that harnessing hierarchicalrelationships enhances the model performance by a large margin. The proposedBayesian head can also be incorporated as a portable module in existing scenegraph generation algorithms to improve their results. In addition, thecommonsense validation enables the model to generate an extensive set ofreasonable predictions beyond dataset annotations.

Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge