Review on scene graph generation methods

Abstract

A scene graph generation is a structured way of representing the image in a graphical network and it is mostly used to describe a scene’s objects and attributes and the relationship between the objects in the image. Image retrieval, video captioning, image generation, specific relationship detection, task planning, and robot action predictions are among the many visual tasks that can benefit greatly from scene graph’s deep understanding and representation of the scene. Even though there are so many methods, in this review we considered 173 research articles concentrated on the generation of scene graph from complex scenes and the analysis was enabled on various scenarios and key points. Accordingly, this research will enable the categorization of the techniques employed for generating the scene graph from the complex scenes that were made based on structured based scene graph generation, Prior knowledge based scene graph generation, Deep understanding based scene graph generation, and optimization based scene graph generation. This survey is based on the research techniques, publication year, performance measures on the popular visual genome dataset, and achievements of the research methodologies toward the accurate generation of scene graph from complex scenes. Towards the end, it identified the research gaps and limitations of the procedures so that the inspirations for introducing an advanced strategy for empowering the advanced generation of graph scenes from the complex scene will the empowered.