# Multi-Modal Low-Data-Based Learning for Video Classification
## 5 Experiments
In the end, the gap between the baseline models, BL CNN and BL ViT, narrows to 5.63%. These results suggest the importance of visual-level feature extraction methodologies in the few-shot learning problem. When analyzing the impact of \(\mathcal{L}_{SS}\) on the few-shot learning problem, BL and I-BL algorithms are compared while taking their visual-level feature extraction methodologies into consideration. On the Densenet-201 side, BL CNN and I-BL CNN, our newly proposed regularization mechanism, improve the classification performance overall by 4.21%. However, especially in the 1-shot setting, which is the crucial and main theme of the few-shot learning problem, improvement is 5.86%. It is important to note that this improvement has been achieved without using any cumbersome network changes or additional network parameter overloading. One noteworthy detail is that using \(\mathcal{L}_{SS}\) regularization decreases the performance gap of Densenet-201 and ViT for all _k-shot_ settings, while using nearly quadruples the efficient neural network architecture. On the ViT side, the few-shot classification accuracy of the ViT models improved by an average of 2.81% across all _k-shot_ settings, and like in the Densenet-201 side analysis, the improvement is nearly 6% in the _5-way 1-shot_ setting, which is a particularly important and challenging setting in real-world applications. Another important analysis is based on the number of classes, _n-way_, in the support set. A general intuition is that when the number of classes increases, the few-shot classification problem becomes more complex and challenging. To evaluate the effect of this, Table 4 includes the results when \(k\) is fixed to 1, and \(n\) goes from 3 to 10, where \(k\) is the number of samples in the support set and \(n\) is the number of classes. As shown in Table 4, \(\mathcal{L}_{SS}\) regularization improves both the Densenet-201-based few-shot learning classification problem by an average of 5.55% across all _n-way_ settings and the ViT-based few-shot learning classification problem by an average of 4.99% across all _n-way_ settings in the 1-shot setting. Also, we have analyzed the impact of \(\mathcal{L}_{SS}\) for different levels. In this context, estimated distributions for classes have a crucial value in performing distribution distance-based classification (DDC), as shown in Section 4.4.2. In this manner, we analyze the \(\mathcal{L}_{SS}\) objective to understand its impacts on low-data-based video classification. In this context, Table 5 shows the effect of SDEM, as shown in Section 4.5, on the estimated distribution parameters. In this experiment, we set the other hyper-parameters as fixed to observe the impact clearly. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{_n-Way_} & **BL** & **I-BL** & **BL** & **I-BL** \\  & **CNN** & **CNN** & **ViT** & **ViT** \\ \hline
3-way & 63.91 & 69.26 & 76.43 & 79.43 \\
5-way & 51.42 & 57.28 & 65.59 & 71.05 \\
10-way & 37.68 & 43.11 & 52.38 & 58.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of _1-shot_ few-shot video classification on the meta-test set for different _n-way_ settings. (Results are presented in percentage). \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **1** & **3** & **5** & **10** & **Average** \\  & **Shot** & **Shot** & **Shot** & **Shot** & **Average** \\ \hline \(\gamma\) = 0.0 (BL) & 51.42 & 71.38 & 77.25 & 82.88 & 70.73 \\ \(\gamma\) = 0.25 & 60.91 & 78.54 & 82.68 & 85.77 & 76.97 \\ \(\gamma\) = 0.50 & 63.70 & 77.96 & 82.10 & 84.80 & 77.14 \\ \(\gamma\) = 0.75 & 47. As observed in Table 5, the estimated distribution parameters become more separable and distinguishable when the \(\mathcal{L}_{SS}\) objective function is used. For example, in the _5-way 1-shot_ setting, whereas the baseline model that does not have the \(\mathcal{L}_{SS}\) objective function achieves 51.42% accuracy, our model, SS-CADA-VAE, has 63.70% accuracy when the coefficient of \(\mathcal{L}_{SS}\) equals 0.5.