# Attention Score Enhancement Model Through Pairwise Image Comparison
## 3 Results  
Table 1 presents the performance evaluation results of the PAE model under various ASMS (Attention Score Map Median Cosine Similarity) threshold settings. The performance metrics used here include precision, recall, F1-Score, and accuracy. Precision indicates how accurately the model predicts a specific class, while recall represents the detection rate for the actual positive class. The F1-Score, which is the harmonic mean of the precision and recall, is useful for evaluating the balance between these two metrics, and the accuracy reflects how well the model classifies the entire dataset. When the ASMS threshold is set to 0.9, the PAE model requires a high similarity between the reference and training images. This setting makes the model more selective in choosing reference images, ensuring greater accuracy when merging reference images with training images. Indeed, at an ASMS threshold of 0.9, the model's precision is high at 0.9333, and recall shows a good performance of 0.8883. The F1-Score at this threshold is 0.91, and the accuracy is 92.32%, suggesting that a higher threshold setting tends to increase the model's predictive reliability. Conversely, when the ASMS threshold is lowered to 0.5, the criteria for selecting reference images become less stringent, increasing the model's sensitivity but somewhat reducing precision. With an ASMS threshold of 0.5, the precision decreases to 0.9216, and the recall records a relatively lower value of 0.8766. The F1-Score at this setting is 0.8966, and the accuracy drops slightly to 92.55%. This indicates that while a lower ASMS threshold increases the model's sensitivity, it may have limitations in maintaining accuracy and precision. Notably, when the ASMS threshold is set to 0.7, the model achieves the most balanced performance. In this case, the precision is 0.9383, recall is 0.8916, F1-Score is 0.9133, and accuracy is at its highest at 92.69%. This suggests that an ASMS threshold of 0.7 provides an optimal setting for balanced performance, achieving high precision and appropriate recall while optimizing both the F1-Score and accuracy. These results underscore the significant impact of ASMS threshold settings on the PAE model's performance. A high ASMS threshold contributes to enhanced predictive reliability by enforcing stricter criteria for selecting reference images, while a lower threshold increases \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ASMS** & **Precision** & **Recall** & **F1-Score** & **Accuracy (\%)** \\ \hline
0.9 & 0.9333 & 0.8883 & 0.91 & 92.32 \\
0.8 & 0.925 & 0.8983 & 0.9133 & 92.53 \\
0.7 & 0.9383 & 0.8916 & 0.9133 & 92.69 \\
0.6 & 0.9366 & 0.88 & 0.9066 & 92.07 \\
0.5 & 0.9216 & 0.8766 & 0.8966 & 92.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance analysis according to ASMS thresholds. the model's sensitivity at the expense of some precision and accuracy. Consequently, the analysis in Table 1 confirms that the ASMS threshold acts as a critical factor in determining the learning characteristics of the PAE model, playing an essential role in enabling the model to accurately learn and classify advanced cognitive functions. Figure 5 visually compares the changes in attention scores before and after enhancement in the PAE model, analyzing its performance on CDT data for Head 1. Figure 5 shows the attention map before attention score enhancement, while Figure 5 visualizes the results after the enhancement. This comparison allows us to observe how the distribution of attention changes. In the attention map shown in Figure 5, the attention focused on specific patches is generally low, with attention values evenly distributed across multiple patches. This indicates that the model does not direct clear attention to visually significant patches, resulting in the inability to effectively capture important visual information. Such a dispersed attention distribution suggests that the model is relatively insensitive to specific visual patterns.