Scene Graph Generation Method Based on External Information Guidance and Residual Scrambling

Scene graphs have become one of the hotspots in computer vision research area due to their characteristics of representing the semantic and organizational structure of visual scene content, which facilitates visual comprehension and interpretable inference. However, due to the imbalance of the relationship annotation between objects in the visual scene, the existing scene graph generation methods are affected by the bias of the data set. The scene graph data imbalance problem is investigated, and a scene graph generation method based on the combination of external information guidance and residual scrambling (EGRES) is proposed to alleviate the negative impact of data set bias on scene graph generation. This method uses unbiased common sense knowledge in the external knowledge base to standardize the semantic space of the scene graph, alleviate the imbalance of the relational data distribution in the data set, and improve the generalization ability of scene graph generation. The residual scrambling method is used to fuse the visual features and the extracted common sense knowledge to standardize the scene graph generation network. The comparison experiments and ablation experiments on the VG data set prove that the proposed method in this paper can effectively improve the scene graph generation. The comparison experiments for different labels in the data set prove that the proposed method can improve the generation performance of most of the relationship categories, especially in the medium and low frequency relationship categories, which greatly alleviates the imbalance of data labeling and has better generation results than the existing scene graph generation methods