中图分类号：TP391
文献标志码：A
DOI: 10.16157/j.issn.0258-7998.223276
中文引用格式： 曾军英，陈运雄，秦传波，等. 基于增强语义信息理解的场景图生成[J]. 电子技术应用，2023，49(5)：52-56.
英文引用格式： Zeng Junying，Chen Yunxiong，Qin Chuanbo，et al. Scene graph generation based on enhanced semantic information understanding[J]. Application of Electronic Technique，2023，49(5)：52-56.

Scene graph generation based on enhanced semantic information understanding

Abstract： The Scene Graph Generation (SGG) task aims to detect visual relation triples in images, i.e. subject, predicate and object, to provide a structural visual layout for scene understanding. However, existing approaches to scene graph generation ignore the high frequency but uninformative problem of predicted predicates, hindering progress in this field. In order to solve the above problems, this paper proposes a scene graph generation algorithm based on enhanced semantic information understanding. The whole model consists of four parts: feature extraction module, image cropping module, semantic transformation module and extended information predicate module. Feature extraction module and image cropping module are responsible for extracting visual features and making them global and diverse. The semantic transformation module is responsible for restoring the semantic relationship between predicates from common predictions to informative predictions. The extended information predicate module is responsible for extending the sampling space of the information predicate. Comparing with other methods on datasets VG and VG-MSDN, the average recall reaches 59.5% and 40.9%, respectively. The algorithm in this paper can improve the problem of insufficient information of the predicted predicate, and then improve the performance of the scene graph generation algorithm.

Key words : scene graph generation；image cropping；semantic transformation；extended information