Attention-Based Scene Graph Generation: A Review

Abstract

The automated creation of a semantic structural scene graph from an image or video is known as scene graph generation (SGG), which includes accurate labeling of all objects that are identified and the interconnections between them. Several SGG methods have been proposed employing deep learning techniques nowadays to achieve good results but most of the approaches failed to integrate the contextual information of pair of objects. Apart from the existing state of the arts of SGG, the attention mechanism is creating a new horizon in this field. This paper offers a thorough analysis of the most recent Attention-Based Scene Graph Generation techniques. In this paper, we have compared and tested five existing Attention-Based Scene Graph Generation methods. We have summarised the results of existing methods to understand progress in this field of Scene Graph Generation. Moreover, we have discussed the strengths of existing techniques and future directions of attention-based models in Scene Graph Generation.