# Soft Contrastive Cross-Modal Retrieval
## 4 Experiment
### Performance Evaluation
#### 4.2.1 Compared with State of the Art  
In this part, we verify the effectiveness of SCCMR by comparing it with 12 baselines on three datasets: Wikipedia, NUS-WIDE, and Pascal Sentence. **Results on Wikipedia. ** We evaluate our proposed method by comparing it with 12 competitors on the Wikipedia dataset. Table 3 reports the mAP scores of the proposed SCCMR and the comparative methods. SCCMR outperforms both the traditional methods and deep learning methods by 61.90% and 49.23% in Text2 Image and Image2 Text tasks, respectively. Notably, SCCMR wins MG-HSF in Text2 Image by mAP = 61.90%, even though MG-HSF defeats it in Image2 Text by mAP = 52.85%. Specifically, our method is higher than MG-HSF in Text2 Image by about 8.7%, which is a giant gap and significant improvement. Meanwhile, it verifies the great performance of SCCMR and proves that the combination of soft contrastive loss and smooth label cross-entropy loss achieves striking success in cross-modal retrieval. On the other hand, except for Deep-SM, DCCA, and DCCAE whose mAPs (Text2 Image mAP = 35.43%, 39.60% and 38.50%; Image2 Text mAP = 39.90%, 44.40%, and 43.50%) are a little lower than JRL (Text2 Image mAP = 41.80%; Image2 Text mAP = 44.90%), the traditional methods perform weaker than deep learning methods in general. The success of SCCMR can largely be credited to its innovative use of soft contrastive learning to discover a suitable common subspace for cross-modal retrieval. This technique allows for more nuanced and adaptable alignment of complex data structures, in contrast to the rigid semantic fusion networks employed by methods such as MG-HSF, which may struggle with the escalating complexity of data. The empirical evidence suggests that SCCMR not only improves the precision of cross-modal retrieval but also enhances the flexibility and scalability of the process, thus confirming its efficacy and potential for broader application in the field. **Results on NUS-WIDE. ** We testify our method by comparing it with 12 state-of-the-art methods on the NUS-WIDE dataset. The mAP scores of our proposed method SCCMR and other competitors on NUS-WIDE are shown in Table 4. Similar to those in the first group of comparisons, SCCMR outperforms all of its competitors by Text2 Image mAP = 69.45% and Image2 Text mAP = 67.97%, respectively. Unexpectedly, the precision of SCCMR is higher than the best competitor MG-HSF in Text2 Image about 4.6% and Image2 Text \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Method** & **Text2 Image** & **Image2 Text** & **Average** \\ \hline \multirow{4}{*}{Traditional Method} & CCA [13] & 17.84 & 21.01 & 19.43 \\  & SM [59] & 28.51 & 23.34 & 25.93 \\  & MCCA [14] & 30.70 & 34.10 & 32.40 \\  & MvDA [28] & 30.80 & 33.70 & 32.30 \\  & MvDA-VC [28] & 35.80 & 38.80 & 37.30 \\  & JRL [29] & 41.80 & 44.90 & 43.40 \\ \hline \multirow{4}{*}{Deep Learning Method} & Deep-SM [33] & 35.43 & 39.90 & 37.67 \\  & CMDN [34] & 42.70 & 48.70 & 45.70 \\  & DCCA [35] & 39.60 & 44.40 & 42.00 \\  & DCCAE [36] & 38.50 & 43.50 & 41.00 \\  & ACMR [32] & 43.42 & 47.74 & 45.58 \\  & MG-HSF [31] & 53.21 & **52.85** & 53.03 \\ \hline \hline \multicolumn{2}{c}{**Ours**} & **61.90** & 49.23 & **55.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance comparison (mAP@50 in %) with 12 competitors on the Wikipedia dataset. The highest performance values are shown in boldface. indeed 6%, which is because MG-HSF pays too much attention to constructing semantic fusion networks to ensure absolute alignment. However, as the complexity of the data structure advances, this undoubtedly increases the difficulty of semantic alignment for each class, while SCCMR discovers a suitable common subspace by soft contrastive learning. Furthermore, the best performance and mAP gap compared with MG-HSF has ascertained the improvement and effectiveness of SCCMR in cross-modal retrieval. The disparity between SCCMR and MG-HSF can be attributed to their core methodological constructs.