# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 4 Experiments
### Comparisons with State-of-the-Art Methods
\begin{table}
\begin{tabular}{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** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline VSE++\({}_{2017}\) & 52.9 & - & 87.2 & 39.6 & - & 79.5 & - \\ SGM\({}_{2020}\) & 71.8 & 91.7 & 95.5 & 53.5 & 79.6 & 86.5 & 478.6 \\ CAMP\({}_{2019}\) & 68.1 & 89.7 & 95.2 & 51.5 & 77.1 & 85.3 & 466.9 \\ SCAN\({}_{2018}\) & 67.4 & 90.3 & 95.8 & 48.6 & 77.7 & 85.2 & 465.0 \\ MMCA\({}_{2020}\) & 74.2 & 92.8 & 96.4 & 54.8 & 81.4 & 87.8 & 487.4 \\ IMRAM\({}_{2020}\) & 74.1 & 93.0 & 96.6 & 53.9 & 79.4 & 87.2 & 484.2 \\ VSRN\({}_{2019}\) & 71.3 & 90.6 & 96.0 & 54.7 & 81.8 & 88.2 & 482.6 \\ GSMN\({}_{2020}\) & 76.4 & 94.3 & 97.3 & 57.4 & 82.3 & 89.0 & 496.8 \\ SGRAF\({}_{2021}\) & 77.8 & 94.1 & 97.4 & 58.5 & 83.0 & 88.8 & 499.6 \\ CMCAN\({}_{2022}\) & 79.6 \({}^{*}\) & 94.0 \({}^{*}\) & 97.4 \({}^{*}\) & 59.6 \({}^{*}\) & 84.1 \({}^{*}\) & 89.2 \({}^{*}\) & 503.9 \({}^{*}\) \\ \hline GGCEN & 80.4 & 96.0 & 97.9 & 60.9 & 84.6 & 90.1 & 509.9 \\  & (0.8\(\uparrow\)) & (1.7\(\uparrow\)) & (0.5\(\uparrow\)) & (1.3\(\uparrow\)) & (0.5\(\uparrow\)) & (0.9\(\uparrow\)) & (6.0\(\uparrow\)) \\ \hline \hline \end{tabular} \({}^{*}\)The values in this table were obtained from our own implementation.  
\end{table} 
Table 1: Comparative analysis of Flickr30K. The best performances are in bold. The symbol (\(\uparrow\)) used in the table signifies the degree of enhancement in model performance. \begin{table}
\begin{tabular}{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** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline VSE++\({}_{2017}\) & 64.6 & 90.0 & 95.7 & 52.0 & 84.3 & 92.0 & 478.6 \\ SGM\({}_{2020}\) & 73.4 & 93.8 & 97.8 & 57.5 & 87.3 & 94.3 & 504.1 \\ CAMP\({}_{2019}\) & 72.3 & 94.8 & 98.3 & 58.5 & 87.9 & 95.0 & 506.8 \\ SCAN\({}_{2018}\) & 72.7 & 94.8 & 98.4 & 58.8 & 88.4 & 94.8 & 507.9 \\ MMCA\({}_{2020}\) & 74.8 & 95.6 & 97.7 & 61.6 & 89.8 & 95.2 & 514.7 \\ IMRAM\({}_{2020}\) & 76.7 & 95.6 & 98.5 & 61.7 & 89.1 & 95.0 & 516.6 \\ VSRN\({}_{2019}\) & 76.2 & 94.8 & 98.2 & 62.8 & 89.7 & 95.1 & 516.8 \\ GSMN\({}_{2020}\) & 78.4 & 96.4 & 98.6 & 63.3 & 90.1 & 95.7 & 522.5 \\ SGRAF\({}_{2021}\) & 79.6 & 96.2 & 98.5 & 63.2 & 90.7 & 96.1 & 524.3 \\ CMCAN\({}_{2022}\) & 78.0 \({}^{*}\) & 96.0 \({}^{*}\) & 98.6 \({}^{*}\) & 63.0 \({}^{*}\) & 90.1 \({}^{*}\) & 95.6 \({}^{*}\) & 521.3 \({}^{*}\) \\ \hline \hline \multirow{2}{*}{GGCEN} & 80.5 & 96.5 & 98.8 & 65.5 & 91.2 & 96.3 & 528.8 \\  & (0.9\(\uparrow\)) & (0.1\(\uparrow\)) & (0.2\(\uparrow\)) & (2.2\(\uparrow\)) & (0.5\(\uparrow\)) & (0.2\(\uparrow\)) & (4.5\(\uparrow\)) \\ \hline \hline \end{tabular} \({}^{*}\)The values in this table were obtained from our own implementation.  
\end{table} 
Table 2: Comparative analysis of MSCOCO 1K test images. The symbol (\(\uparrow\)) used in the table signifies the degree of enhancement in model performance. between the two modalities. Compared with the IMRAM [13] model with an attention network, the GGCEN model introduces a confidence filtering mechanism during local alignment, effectively enhancing meaningful matching information during information exchange between modalities, and ensuring that the focus during global reasoning is on potential semantic relationships between significant aligned features. Additionally, the GGCEN model weighs the local semantic similarity of each region (word) with the inferred confidence and then uses multiple self-attention mechanisms to capture the relationship between the weighted region (word) similarity and the global similarity to generate a more accurate image-text similarity.