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Title: Addressing Computer Vision Challenges Using an Active Learning Framework
Authors: Tzogka, Christina
Refanidis, Ioannis
Editors: Iliadis, L.
Macintyre, J.
Jayne, C.
Pimenidis, E.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Face recognition
Object detection
Active learning
Deep learning
Data set
Issue Date: 1-Jul-2021
Publisher: Springer
First Page: 257
Last Page: 270
Volume Title: Proceedings of the 22nd Engineering Applications of Neural Networks Conference
Part of Series: Proceedings of the International Neural Networks Society
Part of Series: Proceedings of the International Neural Networks Society
Abstract: Computer vision has introduced new successful opportunities in everyday life. Recently, there has been a lot of progress, particularly in face recognition and object detection systems. These systems require a large amount of data to be trained with, in order to perform satisfyingly. Active learning addresses this challenge by leveraging a small amount of manually labelled data. This paper builds on state-of-the-art face recognition and object detection models, by implementing optimization techniques that enhance the recognition accuracy. Further training is being introduced by making use of a robust active learning framework that results in creating extended data sets. Finally, the paper proposes an integrated system, which involves efficient techniques of associating face and object identification information, in order to extract (in real-time) as much knowledge as possible from a video streaming.
ISBN: 978-3-030-80567-8
Electronic ISBN: 978-3-030-80568-5
ISSN: 2661-8141
Electronic ISSN: 2661-815X
Other Identifiers: 10.1007/978-3-030-80568-5_22
Appears in Collections:Department of Applied Informatics

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