# Attention Score Enhancement Model Through Pairwise Image Comparison
## 5 Conclusions  
The PAE model proposed in this study adopts a pairwise learning technique similar to API-Net, but applies the attention score enhancement mechanism more precisely, achieving performance improvements over both API-Net and ViT. In particular, it demonstrated enhanced performance in applications that require focused attention on critical patches in complex datasets, such as CDT data. While API-Net learns visual information based on the similarity between images, the PAE model enhances the attention scores between reference and training images, allowing it to capture more complex and detailed visual features. This differentiated mechanism enables the PAE model to more accurately learn fine visual information that API-Net cannot handle effectively.  
Additionally, the PAE model shows performance improvements compared to the ViT model. Although the ViT excels in learning global patterns of images, it has limitations in capturing local features with precision. In contrast, the PAE model retains the global relationship learning ability of the ViT, while the attention score enhancement mechanism allows it to more effectively capture fine differences in local features. As a result, the PAE model achieved performance improvements in handling local features where the ViT model exhibited shortcomings.  
The proposed PAE model has significant potential for real-world applications, particularly in medical image analysis. By analyzing complex datasets such as CDT, the PAE model can be used to diagnose cognitive impairments at an early stage, which is crucial for the early detection of neurodegenerative diseases such as dementia. The PAE model effectively processes complex visual information and accurately learns key visual features, contributing to improving diagnostic accuracy. Specifically, compared to API-Net and ViT, the PAE model has demonstrated its effectiveness in handling complex visual featuresthrough its superior attention enhancement mechanism. Furthermore, it addresses the key objective of this study, which was to improve the classification accuracy for images located at the boundary of class categories. By learning the subtle differences between visually similar images, the PAE model reduces classification errors for boundary images and significantly improves classification accuracy.  
In conclusion, this study presents the PAE model, which demonstrates significant performance improvements in pairwise learning and attention map enhancement, outperforming both API-Net and ViT particularly in the accurate classification of images near category boundaries. These results indicate that the PAE model has the potential to drive further advancements in visual information processing and analysis. Future work will focus on expanding the model's capabilities and exploring its application in other domains such as autonomous systems, industrial inspection, and security surveillance, with a particular emphasis on enhancing computational efficiency and scalability.  
Conceptualization, J.S.L.; methodology, Y.S.J.; software, Y.S.J.; validation, J.S.L.; formal analysis, Y.S.J.; data curation, Y.S.J.; writing--original draft preparation, Y.S.J.; writing--review and editing, Y.S.J., Z.W.G. and J.S.L.; visualization, Y.S.J.; supervision, Z.W.G. and J.S.L.; project administration, J.S.L.; funding acquisition, J.S.L. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (2021400000060, Department of Next Generation Energy System Convergence based on Techno-Economics--STEP).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.