Scene understanding through Deep Learning
- Recent work in image captioning and scene-segmentation has shown significant results in the context of scene-understanding. However, most of these developments have not been extrapolated to research areas such as robotics. In this work we review the current state-ofthe- art models, datasets and metrics in image captioning and scenesegmentation. We introduce an anomaly detection dataset for the purpose of robotic applications, and we present a deep learning architecture that describes and classifies anomalous situations. We report a METEOR score of 16.2 and a classification accuracy of 97 %.
Document Type: | Report |
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Language: | English |
Author: | Luis Octavio Arriaga Camargo |
Number of pages: | 77 |
ISBN: | 978-3-96043-045-2 |
ISSN: | 1869-5272 |
URN: | urn:nbn:de:hbz:1044-opus-30422 |
DOI: | https://doi.org/10.18418/978-3-96043-045-2 |
Supervisor: | Paul G. Plöger, Matías Valdenegro |
Publishing Institution: | Hochschule Bonn-Rhein-Sieg |
Date of first publication: | 2017/05/29 |
Series (Volume): | Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science (02-2017) |
Keyword: | METEOR score; Scene understanding through Deep Learning; image captioning; robotics; scene-segmentation |
Departments, institutes and facilities: | Fachbereich Informatik |
Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Series: | Technical Report / University of Applied Sciences Bonn-Rhein-Sieg. Department of Computer Science |
Entry in this database: | 2017/05/29 |
Licence (Multiple languages): | In Copyright (Urheberrechtsschutz) |