## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.3 Results of the Multi-Scene Semantic Image Retrieval Experiments  
Using the ten sentences as shown in Table 5, we performed retrieval on the separate models with and without the CAL. During retrieval, we ensured that the texts used involved the semantics of only one scene of the target images; in other words, only parts of the areas of the target images were correlated to the texts for retrieval. The purpose of doing so was to exhibit the influence of multi-scene semantics in RS images on the precision of retrieval as much as possible and to check whether our proposed CABIR could act as a shield against unrelated areas.  
The results in Table 5 show that, after the across attention technique was used, the retrieval rankings of the target images in MSIRTS have risen in most cases. The mean of the rankings has risen from 7.2 to 4.7, and none of the rankings have dropped. This suggests that, while retrieving multi-scene semantic images, the CABIR model proposed in this paper can effectively screen image features unrelated to the current retrieval (as shown in Figure 7), thereby increasing the relevancy between the target image and the texts for the query and enhancing the model's precision.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**RSICD Dataset**} \\ \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Text Retrieval**} & \multicolumn{3}{c}{**Image Retrieval**} \\  & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & **mR** & **R@sum** \\ \hline NO Attention & 4.39 & 10.24 & 17 & 3.27 & 15.04 & 28.12 & 13.01 & 78.06 \\ NO BERT & 9.05 & 15.9 & 22.03 & 4.86 & 16.98 & 29.52 & 16.39 & 98.34 \\ NO GRU & 7.86 & 14.63 & 22.21 & 4.92 & 18.88 & 31.3 & 16.63 & 99.8 \\ NO Temperature & 6.3 & 14.16 & 21.85 & 5.13 & 19.07 & 32.34 & 16.48 & 98.85 \\ Freeze Res152 & 6.39 & 12.34 & 21.76 & 3.91 & 15.87 & 27.77 & 14.67 & 88.04 \\ Freeze BERT & 7.13 & 11.33 & 16.73 & 2.72 & 12.27 & 29.83 & 12 & 80.01 \\ CABIR & 8.59 & 16.27 & 24.13 & 5.42 & 20.77 & 33.58 & 18.12 & 108.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results of all variants of CABIR on the RSICD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Text For Retrieval** & **Target Image** & \
\begin{tabular}{c} **Results Ranking** \\ **(No Attention)** \\ \end{tabular} &
\begin{tabular}{c} **Results Ranking** \\ **(With Attention)** \\ \end{tabular} \\ \hline a plane is parked on the apron & & 3 & 3 \\ \hline there are some roads in the large brown farmland & 6 & 5 \\ \hline there is a large area of neat red buildings in the & & 4 & 2 \\ \hline a playground with several basketball fields & & 5 & 5 \\ \hline beside the highway is a dense residential area & & 13 & 9 \\ \hline next to the sea is a large port & & 5 & 4 \\ \hline There are many vehicles parked in the parking & & 10 & 9 \\ lot around & & & 1 \\ \hline The center is a gray square building & & 1 & 1 \\ \hline There is a forest beside the large farmland & & 22 & 7 \\ \hline a large number of tall trees are planted on both & & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of rankings between the statements for retrieval and the target images used in the experiments.  
Furthermore, there is another phenomenon worth studying throughout the experimental process. In Figure 6e, more areas are related to farmlands than to rivers, but the results of the retrieval indicate that its deep semantic features are more closely related to rivers. We speculate this has something to do with the induction preference of the learner, which may cause higher severity of the mutual interference with multi-scene semantics.