# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 1 Introduction  
Cross-model retrieval focuses on retrieving information from multiple modalities. In the era of intelligent media, emerging social networking sites are gradually becoming more popular, increasing users' expectations for search results. People's demand for information retrieval is no longer satisfied with a single modality, but they want to obtain data from different modalities. Both domestically and globally, academic scholars are committed to exploring the semantic association between cross-modalities to improve the precision and efficiency of retrievals. Cross-modal retrieval faces severe challenges in modal feature representation, complex semantic processing, the alignment of different modal features, and dataset construction. One of the primary motivations for cross-model retrieval is to bridge the gap between the various modalities and enable effective information retrieval. Representation learning is the foundation of cross-modal retrieval. It represents and summarizes the complementarity and redundancy of vision and language. Cross-modal representation in our work explores feature learning and cross-modal interactions for information integration. It intends to minimize redundancy across modalities to provide a more effective feature representation. Cross-modal retrieval explores the mechanisms involved in the transformation of knowledge and seeks to maintain semantic consistency across diverse modalities within the context. Traditional surveys cover various modalities, including text, image, audio, and video modalities. There are also more focused reviews of cross-modal retrieval oriented toward image text. According to the variations in developing inter-modal association methods, traditional studies divided text retrieval approaches into typical association analysis, deeplearning, and deep hashing. They investigated particular remedies to the flaws of cross-modal association methodologies. [PERSON] et al. [1] compared graphic retrieval surveys and analyzed the practical applications. There is low-level representational heterogeneity and high-level semantic homogeneity between vision and language. Multi-learning systems and explored approaches [2] for fusing representations are developed in deep learning architectures. Cross-modal retrieval attempts to realize information interactions by mining the relationships of multiple modal samples. There are more challenges in effective indexing and retrieval. [PERSON] et al. [3] divided cross-modal modeling strategies into direct and indirect modeling. The former directly measured the correlation between different modal data by establishing a sharing layer. The latter established a semantic correlation between other modals in various scenes by building a common representation space. [PERSON] et al. [4] classified existing cross-modal retrieval methods into real-value and binary representation learning and summarized their respective core ideas. However, massive innovative and high-tech breakthroughs have emerged in recent years. Researchers may abandon past machine learning techniques in the deep learning era. [PERSON] et al. [5] paid attention to cross-media analysis and reasoning, which are not carried out by retrieval. With the advancement of cross-modal retrieval, a comparatively high number of review articles emerged. Cross-modal retrieval [6] introduced public space learning and similarity measurement, and different cross-modal retrieval techniques were summarized. Some researchers divide cross-modal retrieval into subspace-based, deep-learning-based, hash-transform-based, and theme-based methods [7]. The main problem is the lack of research on the association between intra-modal local data and inter-modal semantic structures. [PERSON] et al. [8] reviewed cross-modal retrieval models based on representation learning from two dimensions of information extraction. They also provided a summary of feature extraction results.