## 5 Experimentation, Results, and Analysis
### Human Evaluation  
The more nuanced errors, particularly those involving contextual appropriateness and cultural nuances, were identified through detailed human evaluations. These evaluations were conducted by a team of native Tamil speakers who assessed the captions for both linguistic accuracy and contextual relevance. They paid special attention to the translation of specific nouns and the portrayal of actions, as these are crucial in conveying the correct context of the image. Examples of Identified Errors  
For instance, in Table 7, the caption \"A white container filled with a variety of detectable foods\" was translated as \"Glu  
Impact of Errors  
While these errors are minor and do not significantly detract from the overall performance of the DCAT model, they highlight the importance of fine-tuning the model's linguistic understanding and its ability to handle cultural nuances. These insights will be invaluable in guiding future improvements to the model, ensuring more accurate and contextually rich captions. Through this thorough error analysis, we are able to pinpoint specific areas for refinement in the DCAT model, demonstrating our commitment to continuous improvement and excellence in natural language processing and image caption generation. To objectively assess the performance of the proposed DCAT model against baseline models, we conducted a statistical analysis using the paired _t_-test. This test was specifically chosen to compare two related samples--our DCAT model's outputs and those of the baseline models. Rationale for Paired _t_-Test  
The paired _t_-test is ideal for this analysis as it accounts for the inherent similarities between the compared groups (i.e., the same set of images used across all models for captioning). It helps to reduce the variability caused by these similarities, providing a more accurate assessment of the actual differences due to the models themselves. _t_-Test for Composite Evaluation Score (CES)  
We performed the _t_-test on the CES values obtained from the DCAT and baseline models. The null hypothesis (\(H_{0}\)) for this test states that there is no significant difference in CES between DCAT and the baseline models. The alternative hypothesis (\(H_{a}\)) suggests that there is a significant difference, implying the superiority of DCAT. \[H_{0}:\mu_{\text{DCAT}}=\mu_{\text{Baseline}},\quad H_{a}:\mu_{\text{DCAT}} \
eq\mu_{\text{Baseline}} \tag{30}\]  
where \(\mu_{\text{DCAT}}\) and \(\mu_{\text{Baseline}}\) represent the mean CES values of the DCAT and baseline models, respectively. Interpreting _p_-Values  
The _p_-value obtained from the _t_-test quantifies the probability of observing the given data (or more extreme) assuming the null hypothesis is true. A _p_-value less than the chosen significance level (0.05 for a 95% confidence interval) indicates strong evidence against the null hypothesis, leading us to favor the alternative hypothesis. A low _p_-value (<0.05) suggests that the difference in CES between DCAT and the baseline model is statistically significant, indicating the effectiveness of DCAT. A high _p_-value (\(\geq\)0.05) implies that the difference is not statistically significant, indicating that DCAT's performance is comparable to that of the baseline model. The _p_-values in Table 16 indicate the probability of observing the given data if the null hypothesis were true, i.e., if there were no difference in the mean CES scores between DCAT and the respective baseline models. A _p_-value less than 0.05 is generally considered evidence that the data sampled provide sufficient evidence to reject the null hypothesis at the 5% significance level. It can be observed, that the _p_-values for all baseline models are less than 0.05, suggesting that the improvements brought by DCAT are statistically significant. Specifically, the _p_-values for NIC, S2 VT, Up-Down Attention, and Image Transformer are even below 0.01, strengthening the claim that DCAT significantly outperformsthese models.