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.
Document Type
Journal Article
Date of Publication
1-21-2024
Volume
566
Publication Title
Neurocomputing
Publisher
Elsevier
School
Centre for Artificial Intelligence and Machine Learning (CAIML)
RAS ID
62484
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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