OG-SGG: Ontology-Guided Scene Graph Generation-A Case Study in Transfer Learning for Telepresence Robotics Amodeo, Fernando Caballero, Fernando Díaz Rodríguez, Natalia Ana Merino, Luis Scene graph generation Ontology Computer vision Telepresence robotics 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. Speci cally, 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 (speci cally, using the axioms de ned within); and we present results evaluated on a speci c scenario founded in telepresence robotics. These results show quantitative and qualitative improvements in the generated scene graphs. 2023-01-31T08:05:13Z 2023-01-31T08:05:13Z 2022-12-19 info:eu-repo/semantics/article F. Amodeo... [et al.]. "OG-SGG: Ontology-Guided Scene Graph Generation—A Case Study in Transfer Learning for Telepresence Robotics," in IEEE Access, vol. 10, pp. 132564-132583, 2022, doi: [10.1109/ACCESS.2022.3230590] https://hdl.handle.net/10481/79459 10.1109/ACCESS.2022.3230590 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional IEEE