# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 7 Conclusions  
Deep learning research has significantly advanced cross-modal retrieval, providing elegant solutions and driving substantial progress. In this paper, we presented a comprehensive summary and analysis of numerous notable studies and proposed a taxonomy of cross-modal retrieval mechanisms. We also discussed the challenges and open issues to guide future research from a representation learning perspective. To provide a holistic understanding of the full-cycle methodologies, we covered pre-processing, feature engineering, encoding, cross-modal interaction, decoding, model optimization, and evaluation metrics. Additionally, we employed tables, figures, and equations to enhance the clarity of the primary study. Despite the extensive efforts, achieving optimal results and precision in cross-modal retrieval remains an ongoing challenge. Key obstacles include feature representation, complex semantic processing, vision language alignment, unified architecture, model optimization, performance evaluation metrics, and the development of more comprehensive datasets. All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [PERSON] The first draft of the manuscript was written by [PERSON]; Data curation, H.M. and S.T. ; Writing--review & editing, L.Z. and L.S. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\
\begin{tabular}{c} **Pretrain** \\ **Images** \\ \end{tabular} } & \multicolumn{6}{c}{**MS-COCO (5K Test Set)**} & \multirow{2}{*}{
\begin{tabular}{c} **R\_SUM** \\ **TR@1** \\ \end{tabular} } \\ \cline{3-10}  & & & **TR@1** & **TR@5** & **TR@10** & **IR@1** & **IR@5** & **IR@10** \\ \hline UNITER & 4 M & 65.70 & 88.60 & 93.80 & 52.90 & 79.90 & 88.00 & 468.90 \\ ViLT & 4 M & 61.50 & 86.30 & 92.70 & 42.70 & 72.90 & 83.10 & 439.20 \\ ALBEF & 4 M & 73.10 & 91.40 & 96.00 & 56.8 & 81.50 & 89.20 & 488.00 \\ TCL & 4 M & 75.60 & 92.80 & 96.70 & 59 & 83.20 & 89.90 & 497.20 \\ VLC & 5.6 M & 71.30 & 91.20 & 95.80 & 50.70 & 78.90 & 88.00 & 475.90 \\ ALIGN & 1.8 B & 77.00 & 93.50 & 96.90 & 59.90 & 83.30 & 89.80 & 500.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cross-modal retrieval performance comparison on MS-COCO (5K test set). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\
\begin{tabular}{c} **Pretrain** \\ **Images** \\ \end{tabular} } & \multicolumn{6}{c}{**Flickr 30k (1K Test Set)**} & \multirow{2}{*}{
\begin{tabular}{c} **R\_SUM** \\ **TR@1** \\ \end{tabular} } \\ \cline{3-10}  & & & **TR@1** & **TR@5** & **TR@10** & **IR@1** & **IR@5** & **IR@10** \\ \hline SCAN & - & 67.4 & 90.3 & 95.6 & 48.5 & 77.7 & 85.2 & 464.7 \\ CAAN & - & 70.1 & 91.6 & 97.2 & 52.8 & 79.0 & 97.9 & 488.6 \\ VSRN & - & 71.3 & 90.6 & 96.0 & 54.7 & 81.8 & 88.2 & 482.6 \\ MMCA & - & 74.2 & 92.8 & 96.4 & 54.8 & 81.4 & 87.8 & 487.4 \\ CAMRA & - & 78.0 & 95.1 & 97.9 & 60.3 & 85.9 & 91.7 & 508.9 \\ DIME & - & 81.0 & 95.9 & 98.4 & 63.6 & 88.1 & 93.0 & 520.0 \\ UNITER & 4 M & 87.3 & 98.0 & 99.2 & 75.6 & 94.1 & 96.8 & 551.0 \\ VILLA & 4 M & 87.9 & 97.5 & 98.8 & 76.3 & 94.8 & 96.5 & 552.8 \\ ALIGN & 1.8 B & 95.3 & 99.8 & 100 & 84.9 & 97.4 & 98.6 & 576 \\ UNIMO & 5.7 M & 89.4 & 98.9 & 99.8 & 78 & 94.2 & 97.1 & 557.4 \\ VLC & 5.6 M & 89.2 & 99.2 & 99.8 & 72.4 & 93.4 & 96.5 & 550.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-modal retrieval performance comparison on Flickr 30k (1K test set). **Funding:** This work is supported by the National Key Research and Development Program of China (No. 2022 YFC3302103); Fundamental Research Funds for the Central Universities (CUC22 GZ052); National Key R&D Program of China (2020 YFF0305300). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.