# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 1 Introduction
As graph-related knowledge adds to the retrieval process, the cross-modal retrieval method [9] of joint graph regularization was explored. [PERSON] and [PERSON] [10] provided a concise overview of the advancements in cross-modal feature retrieval and optimization. Furthermore, they elucidated the prevalent issues and obstacles encountered in data joint analysis. Compared to preliminary studies, our survey from a representation learning perspective involves full-cycle methodologies that include feature engineering, representation learning, cross-modal interaction, and constructing high-dimensional correlations. The typical survey of cross-modal retrieval is primarily on whether a deep learning model is employed for classification, and then, the retrieval results are analyzed. Different from the traditional works, we focused on the characteristics of deep learning. Taking advantage of pre-training and fine-tuning modes, our survey covers crucial processes of representation, translation, alignment, fusion, co-learning, and cross-modal research. We provide a taxonomy of issues and challenges on the subject to help readers better understand image text and text image retrieval. The survey thoroughly explains how to overcome recent technological breakthroughs. We examined the problems and challenges that must be solved. Figure 1 contains several issues that require addressing. Detailed introductions are provided in the subsequent sections:  
1. Multi-modal data volume: Uni-modal retrieval cannot keep up with the increasing expansion of multi-modal data. 2. Significant differences in the manifestation of heterogeneity: There is an issue with evaluating the content correlation between modal data and computing their similarity. 3. Semantic gaps: The bottom-up semantics in feature analysis between separate modalities are called semantic gaps. 4. Scalability of deep learning models: Adding new datasets, retraining the model, and recalculating all take a long period and need incremental learning. 5. Problem with training datasets: There are missing data, loose data, fewer data labels, and noisy data. Because the volume of particular cross-modal retrieval datasets is now relatively tiny, multi-modal retrieval datasets that are large in scale and a universal representation must be collected. Granularity of cross-modal correlation: Researchers must connect information at the fine-grained and coarse-grained levels and pay close attention to contextual information. * Text length issue: The textual duration in cross-modal retrieval may be too lengthy or too short to prevent it from expressing complete meaning. * Long tail of vision: Raw data in the vision domain often follow a long-tail distribution, with most samples originating from only a small number of classes. * Intra-modal reasoning problem: The fine-grained information in the modal is semantically dependent, posing an intra-modal reasoning challenge. * Inter-modal alignment issue: Fine-grained information alignment and fragment alignment are examples of inter-modal alignment issues. * Scarce memory resources: When dealing with vast amounts of data, real-valued representation techniques suffer from expensive computing costs and great space requirements. * Large latency and low efficiency in retrieval: Extracting region features or other characteristics might be time-consuming, resulting in delayed retrieval results. In addition to covering real-value and binary representation methods, our survey further summarizes deep learning methodologies. Compared to previous work, we summarize the overall workflow of cross-modal retrieval, explore the issues and challenges, and analyze the optimization characteristics in each phase. Our contributions are as follows:  
* We summarize various open issues and challenges. * We concentrated on methodologies with a full-cycle deep learning process, which addresses a gap in existing works. Our approach incorporates innovative techniques and references that are absent in prior surveys.