A Comprehensive Survey on Cross-modal Retrieval
Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang

TL;DR
This survey reviews cross-modal retrieval methods, classifying them into real-valued and binary representation learning, and discusses datasets, experimental results, and future research challenges in the field.
Contribution
It provides a comprehensive classification and comparison of cross-modal retrieval methods, highlighting their characteristics and open research problems.
Findings
Binary methods enable faster retrieval using Hamming space
Real-valued methods learn shared representations for different modalities
Experimental results compare different approaches on benchmark datasets
Abstract
In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation learning. Real-valued representation learning methods aim to learn real-valued common representations for different modalities of data. To speed up the cross-modal retrieval, a number of binary…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Video Analysis and Summarization
