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arXiv · 2308.14263

Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions

Tianshi Wang·Fengling Li·Lei Zhu·Jingjing Li·Zheng Zhang·Heng Tao Shen
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Abstract

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.

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Tianshi Wang, Fengling Li, Lei Zhu, Jingjing Li, Zheng Zhang, Heng Tao Shen. 2023-08-28. Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions. https://arxiv.org/abs/2308.14263

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