# Multi-Modal Low-Data-Based Learning for Video Classification
## 5 Experiments  
We evaluate the SS-CADA-VAE approach on the newly proposed HVU-LD dataset, which includes additional modalities beyond class labels not present in existing video classification datasets, we conducted experiments in both few-shot learning and zero-learning settings for 0-shot, 1-shot, 3-shot, 4-shot, 5-shot, and 10-shot scenarios with varying numbers of support set samples, as well as in 3-way (3 classes), 5-way (5 classes), and 10-way (10 classes) scenarios. In the evaluation stage, we repeat each test result 10 times and report the average accuracy as the final result. Also, we compare several different SS-CADA-VAE hyper-parameters to perform a comprehensive ablation study, testing various combinations of \(\alpha\), \(\beta\), and \(\gamma\) parameters to analyze their impact on low-data-based video classification. We set the baseline model as a CADA-VAE [11] with the same hyper-parameters as the original model, excluding the \(\gamma\) parameter, which is the semi-supervision enhancement module in the SS-CADA-VAE. Additionally, we use the _Distribution Distance-Based Classification_ (DDC) method, as shown in Section 4.4.2, to compare the improvements made by the SS-CADA-VAE more sharply. Behind all those combinations, we also compare the visual and textual feature extraction methods to investigate their impacts on low-data-based video classification. On the other hand, we also evaluated our SS-CADA-VAE model with the previously existing uni-modal dataset in the few-shot learning scenario to verify whether our proposed model is competitive with state-of-the-art few-shot video classification models. Since our model is proposed to provide a baseline for the newly proposed low-data-based multimodal dataset, it should have competitive results with state-of-the-art models, which are proposed for uni-modal-oriented low-data-based studies, so that it can be characterized as a solid starting point for future studies. Impact of Visual Feature Methodologies and Distribution Enhancement Module for Few-Shot Video Classification  
In this analysis section, we present the impact of visual-level feature extraction methods and distribution enhancement module, \(\mathcal{L}_{SS}\), on the low-data-based video classification problem. To produce a fair comparison, textual-level features are set as fixed with ELMO features. In this manner, experiments are divided into two groups in terms of the selected hyper-parameters. The first model type, \(BL\), refers to the baseline model where \(\gamma\) is declined, and the second model type, \(I\)-\(BL\), is the improved baseline model that includes the \(\mathcal{L}_{SS}\) where \(\gamma=1\). The results for _5-way_ few-shot learning are presented in Table 3.  
The analysis of few-shot learning classification can be carried out in two ways: first, the performance of visual-level feature extraction methods without using any additional regularization, which is called the baseline study; second, the contribution of the regularization method \(\mathcal{L}_{SS}\). Also, the number of samples in the support-set, \(k\), is analyzed when making the performance comparison. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Type** & **1-Shot** & **3-Shot** & **5-Shot** & **10-Shot** \\ \hline BL CNN & 51.42 & 71.38 & 77.25 & 82.88 \\ I-BL CNN & 57.28 & 76.23 & 81.22 & 85.03 \\ BL ViT & 65.59 & 82.30 & 85.97 & 88.51 \\ I-BL ViT & 71.05 & 84.89 & 87.81 & 89.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of _5-way_ few-shot video classification on the meta-test set. (Results are presented in percentage). For the first question, for each hyper-parameter group (BL, I-BL), the ViT-based visual-level feature-oriented models outperform the Densenet201-based models' classification accuracy scores for each _k-shot_ group in the few-shot learning problem. On average, the performance gap between visual feature extractors starts at 14.17% when only one sample is present in the support set (_k_ = 1).