dc.creator | Amodeo Zurbano, Fernando | es |
dc.creator | Caballero Benítez, Fernando | es |
dc.creator | Díaz-Rodríguez, Natalia | es |
dc.creator | Merino Cabañas, Luis | es |
dc.date.accessioned | 2023-03-17T18:38:01Z | |
dc.date.available | 2023-03-17T18:38:01Z | |
dc.date.issued | 2022 | |
dc.identifier.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. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/143455 | |
dc.description | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | es |
dc.description.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 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 20 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE ACCESS, 10, 132564-132583. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Scene graph generation | es |
dc.subject | Ontology | es |
dc.subject | Computer vision | es |
dc.subject | Telepresence robotics | es |
dc.title | OG-SGG: Ontology-Guided Scene Graph Generation-A Case Study in Transfer Learning for Telepresence Robotics | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | 2014-2020 | es |
dc.relation.projectID | TELEPORTA. UPO-1264631 | es |
dc.relation.projectID | DeepBot. PY20_00817 | es |
dc.relation.projectID | PLEC2021-007868 | es |
dc.relation.projectID | 10.13039/501100011033 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9991965 | es |
dc.identifier.doi | 10.1109/ACCESS.2022.3230590 | es |
dc.contributor.group | Universidad de Sevilla. TIC255: Service Robotics Laboratory | es |
dc.journaltitle | IEEE ACCESS | es |
dc.publication.volumen | 10 | es |
dc.publication.initialPage | 132564 | es |
dc.publication.endPage | 132583 | es |
dc.contributor.funder | FEDER Andalucía | es |
dc.contributor.funder | Consejería de Economía y Conocimiento de Andalucía | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación. Agencia Estatal de Investigación | es |
dc.contributor.funder | Unión Europea. NextGenerationEU/PRTR | es |