# Supervised Contrastive Learning for 3D Cross-Modal Retrieval
## 4 Experiments
### Qualitative Results  
Visualizations of Experimental Results: To verify the proposed method visually, we exhibit the outcomes of feature clusters and retrieved resultant objects. Figure 3 exhibits the retrieval result of test data in the ModelNet40 dataset using t-distributed stochastic neighbor embedding (T-SNE) [24].  
Furthermore, Figure 4 presents the retrieval results in different modalities. As shown in the figure, it can be observed that the proposed method succeeds in cross-modal retrieval  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Retrieval Tasks**} & \multicolumn{4}{c}{**Methods**} \\ \cline{3-6}  & & & **CLF** & **RONO** & **Ours** \\ \hline Image & & Image & 90.30 & 91.30 & 91.72 \\ Image & & Mesh & 90.70 & 90.60 & 91.47 \\ Image & & Point & 89.50 & 89.80 & 91.64 \\ Mesh & & Image & 88.90 & 89.60 & 90.93 \\ Mesh & \(\rightarrow\) & Mesh & 91.60 & 91.90 & 91.19 \\ Mesh & & Point & 90.00 & 90.40 & 91.20 \\ Point & & Image & 88.70 & 89.50 & 91.35 \\ Point & & Mesh & 89.30 & 90.30 & 91.64 \\ Point & & Point & 88.50 & 89.20 & 92.04 \\ \hline \multicolumn{6}{c}{Average mAP} & 89.72 & 90.29 & **91.46** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The quantitative comparison with previous cross-modal center loss-based methods with a 128 batch size. The number in bold represents the highest average mAP on the ModelNet10 dataset. The highest mAPs for cross-modal retrieval tasks are underlined.  
Figure 3: The visualization result of feature clustering from the ModelNet40 test data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Retrieval Tasks**} & \multicolumn{4}{c}{**Methods**} \\ \cline{3-6}  & & & **CLF** & **RONO** & **Ours** \\ \hline Image & & Image & 90.30 & 91.10 & 90.37 \\ Image & & Mesh & 89.80 & 90.10 & 89.81 \\ Image & & Point & 88.30 & 89.10 & 90.69 \\ Mesh & & Image & 89.10 & 89.90 & 88.15 \\ Mesh & \(\rightarrow\) & Mesh & 87.30 & 90.10 & 88.78 \\ Mesh & & Point & 88.20 & 88.30 & 89.01 \\ Point & & Image & 88.70 & 89.10 & 89.92 \\ Point & & Mesh & 88.30 & 89.40 & 89.88 \\ Point & & Point & 88.10 & 89.10 & 90.88 \\ \hline \multicolumn{6}{c}{Average mAP} & 88.68 & 89.58 & **89.72** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The quantitative comparison with previous cross-modal center loss-based methods with a 128 batch size. The number in bold represents the highest average mAP on the ModelNet40 dataset. The highest mAPs for cross-modal retrieval tasks are underlined.  
regardless of the modality. Based on these results, we can conclude that our method, based on contrastive learning, successfully performs retrieval.  
**Limitations:** We have confirmed that our proposed method successfully performs cross-modal retrieval and achieves state-of-the-art performance. However, as shown in Figure 5, the retrieval also has unresolved issues. Learning is not as effective for classes with a small number of training data. Consequently, additional approaches are required to address the long-tailed issue commonly encountered in deep learning.  
Figure 4: The results of cross-modal retrieval using the proposed method from the ModelNet40 test data.  
Figure 5: The result of cross-modal retrieval on the ModelNet40 test data by class. The illustration depicts sorted classes based on the amount of training data and their corresponding mAPs. In general, results are not favorable for classes with a small (less than 200 in this example) number of training data (i.e., classes included in the blue dotted rectangle).