To know and to learn: about the integration of knowledge representation and deep learning for fine-Grained visual categorization

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.