# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 4 Experiments
### Experimental Results  
We compared the performance of four commonly used computer vision models and architectures for image classification: a CNN [35], VIT [36], ResNet18 [37], and EfficientNet [38]. Specifically, we evaluated their individual performance, as well as their performance when integrated as the image processing module within the Mamba-MDRNet framework on a natural disaster image dataset. The experimental results are presented in Table 1. Here, \(\uparrow\) indicates an improvement in accuracy. The experimental results demonstrate that the accuracy of the CNN, VIT, ResNet18, and EfficientNet improves when they are integrated as the image processing module within the Mamba-MDRNet framework compared to their standalone performance. The accuracy \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Top-1 Accuracy (\%)** & **Improvement (\%)** \\ \hline CNN & 73.35 & \\ Mamba-MDRNet (Image Processing Module is CNN) & 91.75 & \(\uparrow\)18.40 \\ VIT & 68.73 & \\ Mamba-MDRNet (Image Processing Module is VIT) & 94.50 & \(\uparrow\)25.77 \\ ResNet18 & 95.01 & \\ Mamba-MDRNet (Image Processing Module is ResNet18) & 97.25 & \(\uparrow\)2.24 \\ EfficientNet & 95.17 & \\ Mamba-MDRNet (Image Processing Module is EfficientNet) & 97.82 & \(\uparrow\)2.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of different visual models integrated with Mamba-MDRNet. improvements are 18.40%, 25.77%, 2.24%, and 2.65%, respectively. Among these, the VIT model shows the largest improvement when integrated with Mamba-MDRNet, while ResNet18 exhibits the smallest improvement. Further comparison of the classification accuracy of the four models within the Mamba-MDRNet framework reveals that EfficientNet achieves the best classification performance, with accuracy of 97.82%. To further evaluate the impact of multimodal architectures on the model performance, we conducted a series of experiments. These experiments involved both simpler constructions with only three modalities and more complex models combining three modalities using either Transformer or Mamba. We compared their accuracy and runtimes across 10, 20, 30, 40, and 50 training epochs to select the most suitable model. The detailed results are presented in Tables 2 and 3. Overall, the accuracy of the 12 tested models stabilized after 50 epochs. Compared to models handling simple modal information, the inclusion of the Transformer and Mamba modules significantly enhanced their performance. Notably, EfficientNet + TextCNN + MLP + Mamba achieved the highest accuracy of 97.82%. Both ResNet18 and EfficientNet exhibited similar performance after integrating the Transformer, with accuracy of 97.16%. However, after incorporating the Mamba module, EfficientNet showed a greater improvement than ResNet18, with 0.47% higher accuracy. This may be attributed to EfficientNet's architecture, which is designed via neural architecture search (NAS) and better leverages its efficient computational structure when combined with the Mamba module, particularly in long-sequence tasks, resulting in higher accuracy and better resource utilization. While ResNet18 mitigates the vanishing gradient problem in deep networks through residual connections, its simpler design limits its ability to scale and optimize the model parameters dynamically, like EfficientNet. Consequently, ResNet18's fixed structure is less capable of fully utilizing the Mamba module's selective mechanism in complex multimodal tasks. In terms of model efficiency, models incorporating the Transformer and Mamba modules generally took longer to train than simpler models, with those using the Mamba module requiring 1.17% more time than those using the Transformer. This increased training time for the Mamba module is likely due to its selective state-space mechanism, which,