# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 4 Experiments
### Ablation Study  
To demonstrate the efficacy of integrating matching confidence enhancement with global guidance in cross-modal similarity measures, we conducted an assessment of performance on Flickr30K without such enhancements, as outlined in Table 4. In both image and text retrieval, the cross-modal similarity measure that incorporates confidence enhancement and global feature (plus self-attention) outperforms the approach that lacks these enhancements across all metrics. Notably, the inclusion of global guidance leads to improvements in R@1, R@5, and R@10 by 4.0%, 2.9%, and 1.3%, respectively, for text retrieval, and by 4.9%, 3.0%, and 3.4%, respectively, for image retrieval. This indicates that guiding region-word pair matching from a global perspective based on local fine-grained alignment can effectively improve matching performance. This is because the well-represented global features prevent the model from losing global semantics in complex fine-grained local interactions, helping the model deepen its understanding of content and guiding the model to focus on meaningful local matching pairs. From Table 4, it can be seen that after including confidence enhancement, the R@1, R@5, and R@10 text retrieval results show improvements of 4.3%, 2.2%, and 0.8% respectively, while the image retrieval results indicate improvements of 4.0%, 1.3%, and 1.3%. These findings suggest that the confidence scores, which are inferred from the degree of semantic similarity between globally represented text and related images (text) that match semantically, effectively represent the contribution of meaningful region-word matching pairs to the overall similarity in the global context. One possible reason for this may be the incorporation of global information in the region-word relationship reasoning process.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Text Retrieval**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**RSUM**} \\ \cline{2-2} \cline{4-7}  & **R@1** & & & & & & & \\ \hline VSE++2017 & 41.3 & - & 81.2 & 30.3 & - & 72.4 & - \\ SCAN2018 & 50.4 & 82.2 & 90.0 & 38.6 & 69.3 & 80.4 & 410.9 \\ IMRAM2020 & 53.7 & 83.2 & 91.0 & 39.7 & 69.1 & 79.8 & 416.5 \\ SGRAF2021 & 57.8 & - & 91.6 & 41.9 & - & 81.3 & - \\ CMCAN2022 & 57.8 * & 83.9 * & 91.4 * & 41.4 * & 70.9 * & 81.2 * & 426.6 * \\ \hline GGCEN & 60.0 & 86.1 & 92.5 & 43.8 & 72.8 & 83.1 & 438.3 \\  & (2.21) & (2.21) & (0.91) & (1.91) & (1.91) & (1.91) & (1.17) \\ \hline \hline \end{tabular}
* The values in this table were obtained from our own implementation.  
\end{table}
Table 3: Comparative analysis of MSCOCO 5K test images. The symbol (\(\uparrow\)) used in the table signifies the degree of enhancement in model performance.  
Based on these confidence-enhanced features, more accurate matching can be achieved in the process of cross-modal global similarity reasoning. Additionally, the inference is accomplished by averaging the similarity scores of two trained models, which outperform the similarity measurement without confidence enhancement and global feature (plus self-attention) by 19.6% and 14.4% on the overall performance metric R@sum, respectively.