Review of Recent Deep Learning Based Methods for Image-Text Retrieval

Abstract

Cross-modal retrieval has drawn much attention in recent years due to the diversity and the quantity of information data that exploded with the popularity of mobile devices and social media. Extracting relevant information efficiently from large-scale multi-modal data is becoming a crucial problem of information retrieval. Cross-modal retrieval aims to retrieve relevant information across different modalities. In this paper, we highlight key points of recent cross-modal retrieval approaches based on deep-learning, especially in the image-text retrieval context, and classify them into four categories according to different embedding methods. Evaluations of state-of-the-art cross-modal retrieval methods on two benchmark datasets are shown at the end of this paper.