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Author: Francesco Setti

Affiliation: University of Verona, Italy

Keyword(s): Fine-Grained Visual Categorization, Knowledge Representation, Deep Learning, Convolutional Neural Networks, Ontology.

Abstract: Fine-grained visual categorization is becoming a very popular topic for computer vision community in the last few years. While deep convolutional neural networks have been proved to be extremely effective in object classification and recognition, even when the number of classes becomes very large, they are not as good in handling fine-grained classes, and in particular in extracting subtle differences between subclasses of a common parent class. One way to boost performances in this task is to embed external prior knowledge into standard machine learning approaches. In this paper we will review the state of the art in knowledge representation applied to fine-grained object recognition, focusing on methods that use (or can potentially use) convolutional neural networks. We will show that many research works have been published in the last years, but most of them make use of knowledge representation in a very naïve (or even unaware) way.

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Paper citation in several formats:
Setti, F. (2018). To Know and To Learn - About the Integration of Knowledge Representation and Deep Learning for Fine-Grained Visual Categorization. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 387-392. DOI: 10.5220/0006651803870392

@conference{visapp18,
author={Francesco Setti.},
title={To Know and To Learn - About the Integration of Knowledge Representation and Deep Learning for Fine-Grained Visual Categorization},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={387-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006651803870392},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - To Know and To Learn - About the Integration of Knowledge Representation and Deep Learning for Fine-Grained Visual Categorization
SN - 978-989-758-290-5
IS - 2184-4321
AU - Setti, F.
PY - 2018
SP - 387
EP - 392
DO - 10.5220/0006651803870392
PB - SciTePress

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