Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Politécnica de Madrid |
| Repositorio: | Archivo Digital UPM |
| OAI Identifier: | oai:oa.upm.es:70173 |
| Acceso en línea: | https://oa.upm.es/70173/ |
| Access Level: | acceso abierto |
| Palabra clave: | Detección de vehículos detección de objetos ‘few-shot learning’ reconocimiento de minerales segmentación semántica tiempo real = Few-shot learning mineral recognition object detection real-time semantic segmentation vehicle detection |
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oai:oa.upm.es:70173 |
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ES |
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España |
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Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognitionBell Navas, Andrés|||0000-0002-8539-5405Detección de vehículosdetección de objetos‘few-shot learning’reconocimiento de mineralessegmentación semánticatiempo real = Few-shot learningmineral recognitionobject detectionreal-timesemantic segmentationvehicle detectionBlanco Adán, Carlos Roberto del20222022-03-22doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesishttps://oa.upm.es/70173/reponame:Archivo Digital UPMinstname:Universidad Politécnica de MadridInglésenopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:oa.upm.es:701732026-06-21T12:45:07Z |
| dc.title.none.fl_str_mv |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| title |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| spellingShingle |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition Bell Navas, Andrés|||0000-0002-8539-5405 Detección de vehículos detección de objetos ‘few-shot learning’ reconocimiento de minerales segmentación semántica tiempo real = Few-shot learning mineral recognition object detection real-time semantic segmentation vehicle detection |
| title_short |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| title_full |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| title_fullStr |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| title_full_unstemmed |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| title_sort |
Few-shot learning techniques for challenging applications: automatic mineral and vehicle recognition |
| dc.creator.none.fl_str_mv |
Bell Navas, Andrés|||0000-0002-8539-5405 |
| author |
Bell Navas, Andrés|||0000-0002-8539-5405 |
| author_facet |
Bell Navas, Andrés|||0000-0002-8539-5405 |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Blanco Adán, Carlos Roberto del |
| dc.subject.none.fl_str_mv |
Detección de vehículos detección de objetos ‘few-shot learning’ reconocimiento de minerales segmentación semántica tiempo real = Few-shot learning mineral recognition object detection real-time semantic segmentation vehicle detection |
| topic |
Detección de vehículos detección de objetos ‘few-shot learning’ reconocimiento de minerales segmentación semántica tiempo real = Few-shot learning mineral recognition object detection real-time semantic segmentation vehicle detection |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022-03-22 |
| dc.type.none.fl_str_mv |
doctoral thesis http://purl.org/coar/resource_type/c_db06 |
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info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
| dc.identifier.none.fl_str_mv |
https://oa.upm.es/70173/ |
| url |
https://oa.upm.es/70173/ |
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Inglés en |
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Inglés en |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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reponame:Archivo Digital UPM instname:Universidad Politécnica de Madrid |
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Universidad Politécnica de Madrid |
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Archivo Digital UPM |
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Archivo Digital UPM |
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1869403418485850112 |
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15,300719 |