Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval
Tieying Li, Lingdu Kong, Xiaochun Yang, Bin Wang, Jiaxing Xu · Chinese journal of information fusion. · 2024
The rapid advancement of Internet technology, driven by social media and e-commerce platforms, has facilitated the generation and sharing of multimodal data, leading to increased interest in efficient cross-modal retrieval systems. Cross-modal image-text retrieval, encompassing tasks such as image query text (IqT) retrieval and text query image (TqI) retrieval, plays a crucial role in semantic searches across modalities. This paper presents a comprehensive survey of cross-modal image-text retrieval, addressing the limitations of previous studies that focused on single perspectives such as subspace learning or deep learning models. We categorize existing models into single-tower, dual-tower, real-value representation, and binary representation models based on their structure and feature representation. A key focus is placed on the fusion of modalities to enhance retrieval performance across diverse data types. Additionally, we explore the impact of multimodal Large Language Models (MLLMs) on cross-modal fusion and retrieval. Our study also provides a detailed overview of common datasets, evaluation metrics, and performance comparisons of representative methods. Finally, we identify current challenges and propose future research directions to advance the field of cross-modal image-text retrieval.