Relation-Guided Network for Image-Text Retrieval

Abstract

Image-text retrieval has made great progress, but it remains challenging due to heterogeneity between images and text. Enhancing the interaction by exploring the relationship between the image and text can reduce this problem, to some extent. How to explore and use the relationship between image and text to enhance the interaction between them is a critical problem. In this paper, we design an asymmetric structure network (RGN) to represent image and text. First, we mine the relationship between image and text, and extract the specific text information. Then we exploit this relationship to guide the generation of text embeddings, which can capture the rich and representative embeddings. Results on two datasets, Flickr30K dataset and MSCOCO dataset, show that our model can achieve competitive results.