深度学习跨模态图文检索研究综述

随着深度神经网络的兴起,多模态学习受到广泛关注.跨模态检索是多模态学习的重要分支,其目的在于挖掘不同模态样本之间的关系,即通过一种模态样本来检索具有近似语义的另一种模态样本.近年来,跨模态检索逐渐成为国内外学术界研究的前沿和热点,是信息检索领域未来发展的重要方向.首先,聚焦于深度学习跨模态图文检索研究的最新进展,对基于...

Survey of Research on Deep Learning Image-Text Cross-Modal Retrieval

As the rapid development of deep neural networks,multi-modal learning techniques are widely concerned.Cross-modal retrieval is an important branch of multimodal learning.Its fundamental purpose is to reveal the relation between different modal samples by retrieving modal samples with identical semantics.In recent years,cross-modal retrieval has gradually become the forefront and hot spot of academic research.It’s an important direction in the future development of information retrieval.This paper focuses on the latest development of cross-modal retrieval based on deep learning,reviews the development trends of real value representation-based and binary representationbased learning methods systematically.Among them,the real value representation-based method is adopted to improve the semantic relevance,and improve the accuracy,and the binary representation-based learning method is used to improve the efficiency of image-text cross-modal retrieval and reduce storage space.In addition,the common open datasets in the field of image-text cross-modal retrieval are summarized,and the performance of various algorithms on different datasets is compared.Especially,this paper summarizes and analyzes the specified implementations of cross-modal retrieval techniques in the fields of public security,media and medicine.Finally,combined with the state-of-the-art technologies,development trends and future research directions are discussed.