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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Hlavní autor:	Guirguis, Karim
Médium:	Online
Jazyk:	angličtina
Vydáno:	KIT Scientific Publishing 2025
Témata:	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
On-line přístup:	https://library.oapen.org/handle/20.500.12657/100728
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