Scene Graph Generation

Scene graph generation (SGG) aims to represent the objects and their relationships within an image as a graph, providing a structured, semantic understanding of the scene. Current research focuses on improving the accuracy and efficiency of SGG, particularly addressing challenges like long-tailed predicate distributions (where some relationships are far more common than others), bias in predictions, and the need for more efficient model architectures, often employing transformers and graph neural networks. These advancements are significant for various applications, including visual question answering, image captioning, and robotics, by enabling more robust and nuanced scene understanding. Furthermore, the development of large-scale datasets and standardized evaluation metrics is driving progress in the field.