# VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning
## 4 Multimodal Evaluation
### Qualitative Analysis  
To evaluate the contributions of each part and module of VL-Few, we designed another ablation experiment as shown in Table 3. We compared each method in our experiment based on 2-way 1-shot and 5-shots.  
The task instruction includes the task description that could provide the context for the input of the model. The data context provides differentiation ability for input sequences, which can help the model distinguish different modalities and parts, thereby helping the model understand the input sequence. The demonstration context provides task examples to enable the model to mimic the form of tasks and output expected content. These two results show that semantic alignment can improve the semantic understanding of the model.  
The question generation can provide some general questions for datasets that are without questions, which can help the model improve its understanding of images and improve its testing performance. The caption augmentation can construct more data using different captions for one image, which can help alleviate the small sample problem. These two results show that task alignment can help improve the task understanding ability of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \hline
**Method** & **Multimodal Few-Shot Meta Training** & **Crossdomain** & **1-Shot** & **5-Shots** \\ \hline  & \(\times\) & \(\times\) & 6.000 & 7.100 \\ VL-Few & \(\times\) & \(\checkmark\) & 11.160 & 15.328 \\  & \(\checkmark\) & \(\times\) & 7.300 & 8.450 \\  & \(\checkmark\) & \(\checkmark\) & **14.824** & **18.664** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment of comparison of meta and crossdomain of VL-Few on 2-way Real-Fast VQA task in accuracy. The \(\times\) means the methods without meta learning and the \(\checkmark\) means it with the meta learning. The bold font is the best accuracy in the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-4}  & & **1-Shot** & **5-Shots** \\ \hline
1 & multimodal few-shot meta training (w/o) & 11.160 & 15.328 \\
2 & crossdomain (w/o) & 7.300 & 8.450 \\
3 & task instruction (w/o) & 13.064 & 16.752 \\
4 & data context (w/o) & 11.904 & 15.408 \\
5 & demonstration context (w/o) & 12.104 & 16.288 \\
6 & caption augmentation (w/o) & 12.112 & 16.032 \\
7 & question generation (w/o) & 12.096 & 15.928 \\
8 & token-level training (w/o) & 11.808 & 14.088 \\
9 & multitask fusion loss (w/o) & 13.872 & 14.176 \\
10 & VL-Few & **14.824** & **18.664** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment of comparison of VL-Few on 2-way Real-Fast VQA task in accuracy. The bold font is the best accuracy in the results.  
The token-level training can make the model pay attention to each answer token, thus giving more opportunities to the model to learn from the training set. The multitask fusion loss makes the model achieve different objects, which helps the model learn different abilities from different perspectives of the dataset to enhance the overall abilities. These two results show that generation alignment helps the generation ability of the model.