Unbiased Scene Graph Generation Based on Adaptive Regularization Algorithm

The purpose of scene graph generation is to give a picture, obtain the visual triplet form of entities and relationships between entities through the object detection module, namely subject, relationship and object, and construct a semantic structured representation.Scene graphs can be applied to downstream tasks such as image retrieval and visual question answering.However, due to the longtail distribution of relationships between entities in the dataset, existing models tend to predict coarse grained head relationships.Such scene graph cannot play an auxiliary role for downstream tasks.Previous works generally adopt rebalancing strategies such as resampling and reweighting to solve the long tail problem.However, because the models repeatedly learn the tail relationship samples, it is prone to overfitting.In order to solve the above problems, an adaptive regularized unbiased scene graph generation method is proposed in this paper.Specifically, the method adaptively adjusts the weights of full connected class