CGNN: Caption-assisted graph neural network for image-text retrieval

Abstract

• We propose a Caption-Assisted Graph Neural Network (CGNN) for image-text matching. • We generate image captions as auxiliary information to release the domain gap. • Experiments on Flickr30K and MS-COCO show the effectiveness of our framework. Image-text retrieval has drawn much attention in recent years, where similarity measure between image and text plays an important role. Most existing works focus on learning global coarse-grained or local fine-grained features for similarity computation. However, the large domain gap between different modalities is often neglected, which makes it difficult to match the images and texts effectively. In order to deal with this problem, we propose to use auxiliary information to release the domain gap, where the image captions are generated. Then, a Caption-Assisted Graph Neural Network(CGNN) is designed to learn the structured relationships among images, captions, and texts. Since the captions and the texts are from the same domain, the domain gap between images and texts can be effectively released. With the help of caption information, our model achieves excellent performance on two cross-modal retrieval datasets, Flickr30K and MS-COCO, which shows the effectiveness of our framework.