OG-SGG: Ontology-Guided Scene Graph Generation-A Case Study in Transfer Learning for Telepresence Robotics
|Author/s||Amodeo Zurbano, Fernando
Caballero Benítez, Fernando
Merino Cabañas, Luis
|Department||Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática|
|Abstract||Scene graph generation from images is a task of great interest to applications such as robotics,
because graphs are the main way to represent knowledge about the world and regulate human-robot
interactions in tasks such ...
Scene graph generation from images is a task of great interest to applications such as robotics, because graphs are the main way to represent knowledge about the world and regulate human-robot interactions in tasks such as Visual Question Answering (VQA). Unfortunately, its corresponding area of machine learning is still relatively in its infancy, and the solutions currently offered do not specialize well in concrete usage scenarios. Specifically, they do not take existing ‘‘expert’’ knowledge about the domain world into account; and that might indeed be necessary in order to provide the level of reliability demanded by the use case scenarios. In this paper, we propose an initial approximation to a framework called Ontology-Guided Scene Graph Generation (OG-SGG), that can improve the performance of an existing machine learning based scene graph generator using prior knowledge supplied in the form of an ontology (specifically, using the axioms defined within); and we present results evaluated on a specific scenario founded in telepresence robotics. These results show quantitative and qualitative improvements in the generated scene graphs.
|Funding agencies||FEDER Andalucía
Consejería de Economía y Conocimiento de Andalucía
Ministerio de Ciencia e Innovación. Agencia Estatal de Investigación
Unión Europea. NextGenerationEU/PRTR
|Citation||Amodeo Zurbano, F., Caballero Benítez, F., Díaz-Rodríguez, N. y Merino Cabañas, L. (2022). OG-SGG: Ontology-Guided Scene Graph Generation-A Case Study in Transfer Learning for Telepresence Robotics. IEEE ACCESS, 10, 132564-132583. https://doi.org/10.1109/ACCESS.2022.3230590.|