Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems. In this paper, we focus on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. First, we obtain the text instances from images by employing a text reading system. Then, we combine textual features with salient image regions to exploit the complementary information carried by the two sources. Specifically, we employ a Graph Convolutional Network to perform multi-modal reasoning and obtain relationship-enhanced features by learning a common semantic space between salient objects and text found in an image. By obtaining an enhanced set of visual and textual features, the proposed model greatly outperforms the previous state-of-the-art in two different tasks, fine-grained classification and image retrieval in the Con-Text and Drink Bottle datasets.
@article{arxiv.2009.09809,
title = {Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval},
author = {Andres Mafla and Sounak Dey and Ali Furkan Biten and Lluis Gomez and Dimosthenis Karatzas},
journal= {arXiv preprint arXiv:2009.09809},
year = {2020}
}