## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.2 Qualitative Analysis  
To assess the performance of DCAT comprehensively, we conduct a qualitative analysis. This includes a visual comparison of the generated captions with those from baseline models. The analysis spans multiple scenarios such as outdoor activities, human interactions, and complex objects. Tables 7-9 showcase examples from different datasets like Flickr8k, Flickr30k, and MSCOCO, respectively. Moreover, comparisons with other baseline models are presented in Tables 10-12. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Actual Image** & **Model** & **Generated Caption in Tamil** & **Translation to English** \\ \hline \multirow{6}{*}{DCAT (Proposed)} & \multirow{3}{*}{DCAT (Proposed)} & \multirow{3}{*}{DCAT (Proposed)} & \multirow{3}{*}{DATE (Proposed)} & \multirow{3}{*}{Three people} \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline NIC & & & \\  & & & \\  & & & \\  & & & \\ \hline S2 VT & & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{Oscar} & \multirow{3}{*}{DATE (Proposed)} & Three people are on a boat, and there is water around. \\  & & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{AoANet} & \multirow{3}{*}{DATE (Proposed)} & Three people are in a boat on the water area. \\  & & & & water area. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Qualitative analysis: comparison of generated captions with other baseline models for Flickršk dataset. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Actual Image** & **Generated Caption in Tamil** & **Translation to English** \\ \hline \multirow{6}{*}{DCAT (Proposed)} & \multirow{3}{*}{DATE (Proposed)} & \multirow{3}{*}{DATE (Proposed)} \\  & &  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Image** & **Model** & \begin{tabular}{c} **Generated Caption in** \\ **Tamil** \\ \end{tabular}  & \ \begin{tabular}{c} **Translation to** \\ **English** \\ \end{tabular}  \\ \hline \hline \multirow{7}{*}{DCAT (Proposed)} &
\begin{tabular}{c} **Ud/  
DCAT consistently excels in capturing the contextual richness in various scenes. For example, in the Flickr8k dataset (Table 7), DCAT notes the \"vibrant costumes\" and the \"night sky\" for an image of people dancing, adding a poetic element to the description. Similarly, for the Flickr30k dataset (Table 8), DCAT goes beyond merely stating there are \"two men in a park\" and captures the emotional nuance of the scene. The proposed DCAT also outperforms baseline models in capturing the contextual richness of various scenes. For example, in the Flickr8k dataset (Table 10), while baseline models merely describe \"three people on a boat\", DCAT enriches the caption by noting the \"serene boat ride on the lake\" and the \"sunset\", adding layers of context and depth to the description. Furthermore, in the MSCOCO dataset (Table 9), DCAT captures the nuanced relationships between subjects and their environments, as seen in the caption \"An elephant and its baby in a field of grass\", emphasizing both the subjects and the setting in which they exist. Linguistic Accuracy  
The captions generated by DCAT are grammatically correct and employ a varied vocabulary, making them more engaging and informative. Terms like \"vibrant costumes\" and \"delectable foods\" in the Flickr8k dataset (Table 7) indicate high linguistic precision. The captions generated by DCAT also more informative than those from baseline models. In the Flickr30k dataset (Table 11), while baseline models offer simplistic captions like \"two men in a park\", DCAT captures the emotional nuance and detailed actions of the scene, describing \"two joyful men, hand in hand, share a loving moment as they stroll through a serene park\". In the MSCOCO dataset (Table 9), DCAT's caption \"A group of men traveling on horses in the water\" not only describes the main subjects but also indicates their unique activity, showcasing high linguistic precision alongside contextual understanding.