Scene graph generation: A comprehensive survey

Authors

Hongsheng Li
Guangming Zhu
Liang Zhang
Youliang Jiang
Yixuan Dang
Haoran Hou
Peiyi Shen
Xia Zhao
Syed A. A. Shah, Edith Cowan UniversityFollow
Mohammed Bennamoun

Document Type

Journal Article

Publication Title

Neurocomputing

Volume

566

Publisher

Elsevier

School

Centre for Artificial Intelligence and Machine Learning (CAIML)

RAS ID

62484

Comments

Li, H., Zhu, G., Zhang, L., Jiang, Y., Dang, Y., Hou, H., . . . Bennamoun, M. (2024). Scene graph generation: A comprehensive survey. Neurocomputing, 566, article 127052. https://doi.org/10.1016/j.neucom.2023.127052

Abstract

Deep learning techniques have led to remarkable breakthroughs in the field of object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image or a video into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. In this paper, a comprehensive survey of recent achievements is provided. This survey attempts to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Deep discussions about current existing problems and future research directions are given at last. This survey will help readers to develop a better understanding of the current researches.

DOI

10.1016/j.neucom.2023.127052

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.