2023/08/28 by Tianshi Wang, Wang, Tianshi, Fengling Li +9 · 16 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Information Retrieval (cs.IR) #Multimedia (cs.MM) #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.2308.14263
openalex publication_date 2023/08/28 · openalex created_date 2023/08/31 · openalex updated_date 2026/07/28
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between heterogeneous data. Although prior literature has reviewed the field of cross-modal retrieval, it suffers from numerous deficiencies in terms of timeliness, taxonomy, and comprehensiveness. This paper conducts a comprehensive review of cross-modal retrieval's evolution, spanning from shallow statistical analysis techniques to vision-language pre-training models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, the paper delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely-used benchmarks, metrics, and performances. Lastly, the paper probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the ongoing research on cross-modal retrieval, we develop a user-friendly toolbox and an open-source repository at https://cross-modal-retrieval.github.io.