## 4 Experiments
### Comparative Experiments  
Because MCB and ViLT are both modal fusion algorithms, images and attributes were used for fusion, and the fused features were sent to the softmax classifier for learning and classification. The experiment was carried out on the Clothes dataset and the Amazon dataset. The Amazon dataset contained 8600 items in five categories.  
As can be observed in Table 4, although the fusion effect of MCB was the best for the training set and each index was close to 1, its effect was poor for the validation set, with a serious overfitting phenomenon. Compared with Concatenate and ViLT, the accuracy, recall, and \(F_{1}\) value of the verification set of the proposed algorithm improved, but the accuracy was similar. In a specific comparison with Concatenate, the accuracy, recall, and \(F_{1}\) score of the proposed algorithm increased by 10.5%, 11.6%, and 20.9%, respectively; compared with ViLT, the accuracy, recall, and \(F_{1}\) score increased by 16.7%, 54.9%, and 84.8%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**True Value**} & **Total** \\ \cline{3-8}  & & **Clothes** & **Pants** & **Hats** & **Shoes** & \\ \hline \multirow{4}{*}{Predicted value} & Clothes & \(T_{a}\) & \(F_{ba}\) & \(F_{ca}\) & \(F_{da}\) & \(\sideset{}{{}^{\wedge}}{{}_{a}}\) \\ \cline{2-8}  & Pants & \(F_{ab}\) & \(T_{b}\) & \(F_{cb}\) & \(F_{db}\) & \(\sideset{}{{}^{\wedge}}{{}_{b}}\) \\ \cline{2-8}  & Pants & \(F_{ac}\) & \(F_{bc}\) & \(T_{c}\) & \(F_{dc}\) & \(\sideset{}{{}^{\wedge}}{{}_{c}}\) \\ \cline{2-8}  & Shoes & \(F_{ad}\) & \(F_{M}\) & \(F_{cd}\) & \(T_{d}\) & \(\sideset{}{{}^{\wedge}}{{}_{d}}\) \\ \hline Total & \(S_{a}\) & \(S_{b}\) & \(S_{b}\) & \(S_{d}\) & \(T_{t}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
As can be seen in Table 5, MCB still had serious overfitting on the Amazon dataset. The effect of ViLT and Concatenate decreased compared with that for the small dataset, while the algorithm proposed here had a more comparable performance. Compared with Concatenate, the accuracy, recall, and \(F_{1}\) score of the proposed algorithm increased by 10.5%, 11.6% and 20.9%, respectively; compared with ViLT, the accuracy, recall, and \(F_{1}\) score increased by 16.7%, 54.9% and 84.8%, respectively. It can be seen that the performance of the proposed algorithm for accurately representing a product was greatly improved.  
In order to verify the computational complexity of the algorithm, we compared the running time of MCB, ViLT, and the proposed algorithm. We calculated the average value of 100 experiments, and the results are shown in Table 6. The results show that MCB had the shortest running time but the worst effect. Compared with ViLT, the running time of the proposed algorithm improved by 74.1%. In conclusion, the algorithm in this study had low computational complexity under the conditions of high fusion accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Concatenate** & **MCB** & **ViLT** & **Ours** \\ \hline Accuracy & 0.9951 & 1.0000 & 0.8731 & 0.9786 \\ Val\_Accuracy & 0.8750 & 0.2894 & 0.8289 & 0.9671 \\ Precision & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Val\_Precision & 1.0000 & 0.0367 & 1.0000 & 1.0000 \\ Recall & 0.9835 & 1.0000 & 0.6606 & 0.9638 \\ Val\_Recall & 0.8487 & 0.1118 & 0.6118 & 0.9474 \\ \(F_{1}\) & 0.9997 & 0.9999 & 0.7584 & 0.9901 \\ Val\_F1 & 0.8236 & 0.0206 & 0.5388 & 0.9959 \\ \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results on the Clothes dataset. Note: Val_Accuracy, Val_Precision, Val_Recall, and Val_F1 are the accuracy, accuracy, recall, and \(F_{1}\) score, respectively, for the validation set.  
Figure 5: The algorithm’s classification results: (**a**) accuracy, (**b**) recall, and (**c**) \(F_{1}\) score.