## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.2 Results of the Ablative Experiments  
In order to compare the roles of all structural components of CABIR, we designed a total of six variants of CABIR by making separate changes to the following modules:  
1. the CAL, which was replaced with a global average pooling layer, hence the name \"NO Attention\" for the variant resulting from this change;
2. the pretrained BERT network, which was removed and replaced with an embedded matrix to change the dimension of the word vector into 300, hence the name \"NO BERT\" for the variant resulting from this change;
3. the GRU module, which was removed, with the CLC vector of the output sequence of the BERT network taken immediately as statement-level text features and mapped into a 2048-dimensional vector space via a fully connected layer, hence the name \"NO GRU\" for the variant resulting from this change;
4. the temperature control function, which was removed, hence the name \"NO Temperature\" for the variant resulting from this change;
5. the parameters of pretrained RSE152, which were frozen during training, hence the name \"Freeze Res152\" for the variant resulting from this change;
6. the parameters of pretrained BERT, which were frozen during training, hence the name \"Freeze BERT\" for the variant resulting from this change.  
From the data displayed in Table 4, the CAL has the greatest influence on the overall performance of the model, which differs by 39.3% with and without this component. The second greatest influence comes from freezing the parameters of the two pretrained models. The next step is the freezing of the parameters of the two pretraining models, which would at least lead to a performance degradation of above 20%. Despite the fact that previous literature [7] has warned that fine-tuning of pretrained models would increase the risk of overfitting, the experimental results show that training without fine-tuning makes a difference to the generation of optimal joint semantic space of models. The changes into \"NO BERT\" and \"NO GRU\" were intended to verify the effectiveness of the statement-level text feature extraction method proposed in this paper, while the change into \"NO Temperature\" was intended to check whether our proposed temperature control strategy would work. From the experimental data, the removal of whichever of the above modules would lead to a performance difference of above 10%, which is undoubtedly a piece of strong evidence. The above results fully show that our designed CABIR model is complete  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**UCM Dataset**} \\ \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Text Retrieval**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{} \\  & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & **mR** & **R@sum** \\ \hline VSE++ & 24.14 & 53.45 & 67.24 & 6.21 & 33.56 & 51.03 & 39.27 & 235.63 \\ SCAN t2i & 18.97 & 51.72 & 74.14 & 17.59 & 56.9 & 76.21 & 49.26 & 295.53 \\ SCAN i2t & 20.69 & 55.17 & 67.24 & 15.52 & 57.59 & 76.21 & 48.74 & 292.42 \\ MTN & 20.69 & 51.72 & 68.97 & 13.79 & 55.51 & 77.59 & 48.05 & 288.27 \\ AMFMN-soft & 20.69 & 51.72 & 74.14 & 15.17 & 58.62 & 80 & 50.06 & 300.34 \\ AMFMN-fusion & 24.14 & 51.72 & 75.86 & 14.83 & 56.55 & 77.89 & 50.17 & 300.99 \\ AMFMN-sim & 29.31 & 58.62 & 67.24 & 13.45 & 60 & 81.72 & 51.72 & 310.34 \\ CABIR & 32.76 & 65.52 & 79.31 & 19.66 & 57.59 & 78.51 & 55.53 & 333.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results of all models on the Sydney-Captions dataset.  
and reasonable in system structure, with all components existing indispensably, being closely connected, and interplaying with one another.