Scene graph generation(SGG)plays an important role in deep visual understanding tasks.Existing SGG methods main-ly focus on the locations and categories of objects,as well as the relationship between objects,while ignoring that the object at-tributes also contain rich semantic information.This paper proposes a SGG model integrating with the object attributes.Firstly,to achieve multi-label object attribution recognition,we propose the composite classifiers that combine the multi-class classification trained by improved group cross entropy loss and binary classification trained by binary cross entropy loss,which can improve the accuracy and recall of multiple attribute predictions.Then,the branch of attribution recognition is fused into the SGG framework.As a kind of context information,the attribution features are fed into the relationship branch for better relationship classification.Finally,compared with several baseline models,our method has achieved better performance in both object attribute prediction and relationship recognition on VG150 dataset.
关键词
场景图生成/对象属性识别/属性融合/关系预测/多标签分类/团组交叉熵函数
Key words
Scene graph generation/Object attribute recognition/Attribute fusion/Relationship classifications/Multi-label lear-ning/Group cross entropy function
维普
万方数据