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dc.contributor.authorCartas Ayala, Alejandro
dc.contributor.authorRadeva, Petia
dc.contributor.authorDimiccoli, Mariella
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2021-09-14T09:28:40Z
dc.date.available2021-09-14T09:28:40Z
dc.date.issued2021
dc.identifier.citationCartas, A.; Radeva, P.; Dimiccoli, M. Modeling long-term interactions to enhance action recognition. A: International Conference on Pattern Recognition. "Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition: Milan, 10–15 January 2021". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 10351-10358. ISBN 978-1-7281-8808-9. DOI 10.1109/ICPR48806.2021.9412148.
dc.identifier.isbn978-1-7281-8808-9
dc.identifier.urihttp://hdl.handle.net/2117/351241
dc.description© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractIn this paper, we propose a new approach to understand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical Long Short-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks, without relying on motion information.
dc.description.sponsorshipThis work was partially supported by CONACYT grant 366596, TIN2018-095232-B-C21, SGR-2017 1742, Nestore project of the European Commission Horizon 2020 programme (Grant No769643), Validithi EIT Health program and CERCA Programme/Generalitat de Catalunya, MINECO/ERDF-EU through the program Ramon y Cajal, projects PID2019-110977GA-I00 and RED2018-102511-T. We thank the support of NVIDIA Corporation for hardware donation
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherPattern recognition
dc.titleModeling long-term interactions to enhance action recognition
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1109/ICPR48806.2021.9412148
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9412148/
dc.rights.accessOpen Access
local.identifier.drac31844658
dc.description.versionPostprint (author's final draft)
local.citation.authorCartas, A.; Radeva, P.; Dimiccoli, M.
local.citation.contributorInternational Conference on Pattern Recognition
local.citation.publicationNameProceedings of ICPR 2020: 25th International Conference on Pattern Recognition: Milan, 10–15 January 2021
local.citation.startingPage10351
local.citation.endingPage10358


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