Towards Learning Object Detectors with Limited Data for Industrial Applications
In this dissertation, three novel Generalized Few-Shot Object Detection (G-FSOD) approaches are presented to minimize the forgetting of previously learned classes while learning new classes with limited data. The first two approaches reduce the forgetting of base classes if they are still available...
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| Médium: | Online |
| Jazyk: | angličtina |
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KIT Scientific Publishing
2025
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| On-line přístup: | https://library.oapen.org/handle/20.500.12657/100728 |
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| _version_ | 1869522104050778112 |
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| author | Guirguis, Karim |
| author_browse | Guirguis, Karim |
| author_facet | Guirguis, Karim |
| author_sort | Guirguis, Karim |
| collection | Directory of Open Access Books |
| description | In this dissertation, three novel Generalized Few-Shot Object Detection (G-FSOD) approaches are presented to minimize the forgetting of previously learned classes while learning new classes with limited data. The first two approaches reduce the forgetting of base classes if they are still available during training. The third approach, for scenarios without base data, uses knowledge distillation to improve the knowledge transfer. |
| format | Online |
| id | doab-20.500.12854ir-158411 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1584112025-05-27T05:22:37Z Towards Learning Object Detectors with Limited Data for Industrial Applications Guirguis, Karim Optical Inspection; Object Detection; Deep Learning; Few Shot Learning; Optische Inspektion; Objekt-Erkennung; Computer Vision; Tiefes Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists In this dissertation, three novel Generalized Few-Shot Object Detection (G-FSOD) approaches are presented to minimize the forgetting of previously learned classes while learning new classes with limited data. The first two approaches reduce the forgetting of base classes if they are still available during training. The third approach, for scenarios without base data, uses knowledge distillation to improve the knowledge transfer. 2025-04-15T04:05:56Z 2025-04-15T04:05:56Z 2025-04-14T12:06:32Z 2025 book https://library.oapen.org/handle/20.500.12657/100728 9783731513896 https://directory.doabooks.org/handle/20.500.12854/158411 eng Schriftenreihe Automatische Sichtprüfung und Bildverarbeitung open access image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/100728/1/towards-learning-object-detectors-with-limited-data-for-industrial-applications.pdf https://library.oapen.org/bitstream/20.500.12657/100728/1/towards-learning-object-detectors-with-limited-data-for-industrial-applications.pdf KIT Scientific Publishing 10.5445/KSP/1000174849 10.5445/KSP/1000174849 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513896 262 open access |
| spellingShingle | Optical Inspection; Object Detection; Deep Learning; Few Shot Learning; Optische Inspektion; Objekt-Erkennung; Computer Vision; Tiefes Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists Guirguis, Karim Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title | Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title_full | Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title_fullStr | Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title_full_unstemmed | Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title_short | Towards Learning Object Detectors with Limited Data for Industrial Applications |
| title_sort | towards learning object detectors with limited data for industrial applications |
| topic | Optical Inspection; Object Detection; Deep Learning; Few Shot Learning; Optische Inspektion; Objekt-Erkennung; Computer Vision; Tiefes Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| topic_facet | Optical Inspection; Object Detection; Deep Learning; Few Shot Learning; Optische Inspektion; Objekt-Erkennung; Computer Vision; Tiefes Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| url | https://library.oapen.org/handle/20.500.12657/100728 |
| work_keys_str_mv | AT guirguiskarim towardslearningobjectdetectorswithlimiteddataforindustrialapplications |