Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval

• URL: http://arxiv.org/abs/2009.09809v1
• Date: Mon, 21 Sep 2020 12:31:42 GMT
• Title: Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
• Authors: Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez and Dimosthenis Karatzas
• Abstract summary: This paper focuses on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. 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.
• Score: 8.317191999275536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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