Scene Graph Generation: A comprehensive survey

Hongsheng Li, Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Xia Zhao, Syed Afaq Ali Shah, Mohammed Bennamoun

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Article number127052
Number of pages25
JournalNeurocomputing
Volume566
Early online date20 Nov 2023
DOIs
Publication statusPublished - 21 Jan 2024

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