# Soft Contrastive Cross-Modal Retrieval
## 4 Experiment
### Performance Evaluation
#### 4.2.1 Compared with State of the Art
MG-HSF does not scale well with the increasing complexity of data structures, which intensively constructs semantic fusion networks to guarantee precise alignment. This complexity amplifies the difficulty of achieving semantic alignment across diverse classes, a challenge that the rigid MG-HSF alignment system may not effectively address. Conversely, SCCMR introduces a soft contrastive learning paradigm that adeptly identifies a suitable common subspace. This flexible approach accommodates the intricate variances within the data, allowing enhanced semantic bridging between modalities without necessitating an absolute alignment of features. Hence, SCCMR not only ensures superior alignment between text and image data but also demonstrates adaptability to complex data structures. The results underscore the significant advancements SCCMR brings to cross-modal retrieval. The method's performance indicates its capability to generalize across modalities and effectively manage the multifaceted nature of the NUS-WIDE dataset. The mAP gap between SCCMR and MG-HSF solidifies the argument for the effectiveness and innovation of our approach. In conclusion, the pioneering SCCMR soft contrastive learning approach provides a robust solution for cross-modal retrieval tasks. The empirical results on the NUS-WIDE dataset validate its leadership position, opening avenues for further research and application in diverse scenarios requiring nuanced semantic understanding across different data types. **Results on Pascal Sentence. ** Our SCCMR method's performance has been rigorously evaluated against 12 state-of-the-art methods on the Pascal Sentence dataset as evidenced in Table 5. The table presents the mAP scores in percentages, with the highest values emphasized in bold. For the Text2 Image retrieval, SCCMR achieves a mAP score of 69.55%, and that is significantly competitive, though it is not the highest, trailing by a narrow margin of 1.8% from the leading MG-HSF score of 71.55%. In the Image2 Text retrieval task, our method secures a mAP score of 68.33%, which is only 1.29% shy of the leading MG-HSF score of 69.62%. The average performance of SCCMR is 68.94%, underscoring its robustness across both retrieval directions. When delving deeper into the comparative analysis, it is notable that SCCMR surpasses all traditional methods by a substantial \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Method** & **Text2 Image** & **Image2 Text** & **Average** \\ \hline \multirow{8}{*}{Traditional Method} & CCA [13] & 36.80 & 38.17 & 37.49 \\  & SM [59] & 42.37 & 39.16 & 40.77 \\  & MCCA [14] & 46.20 & 44.80 & 45.50 \\  & MvDA [28] & 52.60 & 50.10 & 51.30 \\  & MvDA-VC [28] & 55.70 & 52.60 & 54.20 \\  & JRL [29] & 59.80 & 58.60 & 59.20 \\ \hline \multirow{8}{*}{Deep Learning Method} & Deep-SM [33] & 62.55 & 57.80 & 60.18 \\  & CMDN [34] & 51.50 & 49.20 & 50.40 \\  & DCCA [35] & 54.90 & 53.20 & 54.00 \\  & DCCAE [36] & 54.00 & 51.11 & 52.50 \\  & ACMR [32] & 57.85 & 58.41 & 58.13 \\  & MG-HSF [31] & 64.88 & 62.06 & 63.47 \\ \hline \multicolumn{2}{c}{**Ours**} & **69.45** & **67.97** & **68.71** \\ \hline \end{tabular}
\end{table} 
Table 4: The performance comparison (mAP@50 in %) with 12 competitors on NUS-WIDE dataset. The highest performance values are shown in boldface. margin, indicating the advantages of leveraging deep learning for feature extraction and semantic understanding. Among traditional methods, MCCA shows the next best average performance of 67.70%, which our SCCMR method outperforms by 1.24%. Among deep learning methods, the performance of our SCCMR is highly competitive. It is important to note that while MG-HSF leads in performance, the difference is narrow, and given the complexity of the data structure in the Pascal Sentence dataset, this gap indicates a significant achievement for SCCMR. The slight edge of MG-HSF can be attributed to its intense focus on constructing semantic fusion networks for alignment.