Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
This work addresses the problem of improving fine-grained image analysis for computer vision applications by integrating multi-modal data, representing an incremental advance over existing methods.
The paper tackles fine-grained image classification and retrieval by leveraging both visual and textual cues from scene text in images, using a Graph Convolutional Network to combine these modalities, resulting in outperforming previous state-of-the-art methods on the Con-Text and Drink Bottle datasets.
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.