## 5 Experimentation, Results, and Analysis
### Analysis  
#### 5.4.1 Quantitative Analysis  
To supplement our qualitative findings, we conducted an exhaustive quantitative analysis. The metrics used for this purpose are BLEU, METEOR, CES, and CA, as defined in Section 5.2. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & **BLEU** & **METEOR** & **CES** & **CA** \\ \hline \multirow{3}{*}{DCAT (Proposed)} & Flickr8k & 0.7425 & 0.4391 & 0.6279 & 0.4182 \\  & Flickr30k & 0.7388 & 0.4356 & 0.6243 & 0.4129 \\  & MSCOCO & 0.7366 & 0.4329 & 0.6215 & 0.4096 \\ \hline \multirow{3}{*}{NIC} & Flickr8k & 0.5829 & 0.3245 & 0.4678 & 0.2522 \\  & Flickr30k & 0.5803 & 0.3229 & 0.4653 & 0.2505 \\  & MSCOCO & 0.5787 & 0.3213 & 0.4637 & 0.2491 \\ \hline \multirow{3}{*}{S2 VT} & Flickr8k & 0.6061 & 0.3437 & 0.4940 & 0.2708 \\  & Flickr30k & 0.6043 & 0.3421 & 0.4921 & 0.2692 \\  & MSCOCO & 0.6025 & 0.3405 & 0.4902 & 0.2676 \\ \hline \multirow{3}{*}{Up-Down Attention} & Flickr8k & 0.6449 & 0.3747 & 0.5235 & 0.2972 \\  & Flickr30k & 0.6431 & 0.3731 & 0.5216 & 0.2956 \\  & MSCOCO & 0.6413 & 0.3715 & 0.5197 & 0.2940 \\ \hline \multirow{3}{*}{Image Transformer} & Flickr8k & 0.6634 & 0.3901 & 0.5405 & 0.3123 \\  & Flickr30k & 0.6616 & 0.3885 & 0.5386 & 0.3107 \\  & MSCOCO & 0.6598 & 0.3869 & 0.5367 & 0.3091 \\ \hline \multirow{3}{*}{Oscar} & Flickr8k & 0.6896 & 0.4131 & 0.5649 & 0.3272 \\  & Flickr30k & 0.6878 & 0.4115 & 0.5630 & 0.3256 \\  & MSCOCO & 0.6860 & 0.4099 & 0.5611 & 0.3240 \\ \hline \multirow{3}{*}{AoAANet} & Flickr8k & 0.7135 & 0.4304 & 0.5935 & 0.3427 \\  & Flickr30k & 0.7117 & 0.4288 & 0.5916 & 0.3411 \\ \cline{1-1}  & MSCOCO & 0.7099 & 0.4272 & 0.5897 & 0.3395 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of proposed DCAT with baseline models across datasets. Performance Over Epochs  
We observed the performance of DCAT over multiple training epochs. As shown in Figure 2, the model reaches peak performance at around the 40 th epoch, beyond which the gains are minimal, validating our choice of training for 50 epochs. We observe distinct trends for each dataset and metric. For Flickr8k and Flickr30k, the model stabilizes around the 40 th epoch for all metrics, which justifies our choice of training for 50 epochs. For MSCOCO, the model reaches peak performance earlier, around the 30 th epoch, making it an interesting case where performance stabilizes more quickly. * **BLEU:** The BLEU score for Flickr8k, Flickr30k, and MSCOCO reaches 0.7425, 0.7388, and 0.7366, respectively. This shows the model's consistent performance across various datasets. * **METEOR:** METEOR scores exhibit a similar trend, stabilizing at 0.4391 for Flickr8k, 0.4356 for Flickr30k, and 0.4329 for MSCOCO. * **CES:** The Composite Evaluation Score (CES) for Flickr8k, Flickr30k, and MSCOCO stabilizes at 0.6279, 0.6243, and 0.6215, respectively. The trend follows closely with the individual metric scores. * **CA:** The Contextual Accuracy (CA) measure also shows consistent improvement over epochs, reaching 0.4182 for Flickr8k, 0.4129 for Flickr30k, and 0.4096 for MSCOCO. Effect of Hyperparameters  
We conducted a sensitivity analysis to understand the impact of different hyperparameters on the Composite Evaluation Score (CES) of the DCAT model. The results are visually represented in Figure 3.  
Figure 2: Performance of DCAT over training epochs. * **Learning Rate:** As observed in the first subplot, the model performance is relatively robust to the learning rate, reaching peak performance at \(1\times 10^{-4}\). Beyond this point, the CES starts to decline, indicating that a learning rate that is too high may hinder the model's ability to converge to an optimal solution. * **Batch Size:** The second subplot shows that the model's performance is fairly stable across different batch sizes, with a slight peak at a batch size of 64. This underscores the model's robustness to the batch size, within the tested range.