F. Gouidis, A. Vassiliades, T. Patkos, A. Argyros, N. Bassiliades, D. Plexousakis. A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning. presented at AAAI Spring Symposium, Stanford University, Palo Alto, California, USA,March 23-25, 2020

Author(s): F. Gouidis, A. Vassiliades, T. Patkos, A. Argyros, N. Bassiliades, D. Plexousakis

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Appeared In: presented at AAAI Spring Symposium, Stanford University, Palo Alto, California, USA,March 23-25, 2020

Keywords: Computer Vision and Pattern Recognition, Artificial Intelligence, Machine Learning, Semantic Web

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Abstract: Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results. While Machine Learning has been traditionally applied to address related problems, recent works also seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects, their properties and their relations with their environment. In this paper, we attempt a systematic investigation of how knowledge-based methods contribute to diverse object perception tasks. We review the latest achievements and identify prominent research directions.

See Also: SoCoLA