## 4 Discussion
### Influence of Different Image Region Divisions on Model Precision  
As mentioned above, for ResNet152, when the image size was 224 \(\times\) 224, the image would be divided into 49 areas. Evidently, this parameter could be adjusted by changing either the network structure or the image size. For instance, adding a pooling layer for downsampling would decrease the number of regions, while using higher-resolution images would increase the number of regions. From the perspective of the mechanism, the number of regions can neither get too large nor too small--too fine-grained a division would extremely likely cause a target to be segmented into multiple regions, while too coarse-grained a division would go against semantic distinction. In this discussion, we changed the number of image areas to 36, 25, 16, 9, and 4 (on the dataset RSICD), respectively, by adding a convolutional layer to ResNet152 and observed the variations in model precision. From Figure 9, the number of image areas has a great influence on the model precision, which first decreases fast with the decrease in the number of areas and then keeps relatively stable at a low precision.  
Figure 8: Variations of model precision with temperature control parameters.  
The above results seem to mean that a finer-grained region division is more advantageous to image-text semantic association--which disagrees with the foregoing analysis. However, we guessed it might be that the 49 regions divided have not yet reached the turning point. Thus, we conducted another control experiment over the Sydney-Captions datasets--whose image was larger, with a resolution of 500 \(\times\) 500, and the number of original regions processed by ResNet152 was 16 \(\times\) 16. In the experiment, the number of image regions varied within [144, 100, 64, 49, 36]. According to the experimental results in Figure 10, a greater number of regions (16 \(\times\) 16) corresponds to a better result, even though the intergroup difference has decreased significantly. The differences between the region divisions of 12 \(\times\) 12 and 8 \(\times\) 8 and the optimal result are 3.9% and 3.3%, respectively. Moreover, from the results of these two divisions, we did observe the phenomenon that \"the number of regions decreases while the model precision increases\". Why it is that 16 \(\times\) 16 number of regions achieves the optimal result? We suggest two possibilities: (1) The 16 \(\times\) 16 region division has not yet reached the turning point enough to degrade the model's performance; (2) the newly added convolutional layer has not undergone pretraining, which may have interfered with precision. This reminds us that the pretraining model of ResNet152 is not completely coincident with our method and that a pretraining convolutional architecture that can be adapted to scale variations of images is quite crucial to CABIR.  
Figure 10: Variations of model precision over the Sydney-Captions dataset with the number of image areas.  
Figure 9: Variations of model precision over RSICD dataset with the number of image areas.  
It should also be noted that a larger number of image areas means a higher computation overhead. From our recorded experimental data, with the continuous increase in the number of areas, the computational time cost increases by a percentage of 25% to 50%. In practical applications, it is necessary to balance accuracy and efficiency.