## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.3 Visual Explanation Using Grad-CAM
###### Evaluation Sets  
The evaluation was conducted on five sets of 100 randomly selected samples each, sourced from the Flickr8k, Flickr30k, and MSCOCO datasets. This approach balanced the need for comprehensive evaluation with practical constraints.  
Table 14 presents the human evaluation scores for five sets of 100 samples each. The choice of five sets with 100 samples was made to balance the need for comprehensive evaluation with the practical constraints of human evaluation. Here, we provide a detailed analysis of the results.  
\begin{table}
\begin{tabular}{c l l} \hline
**Score** & **Adequacy Scoring Criteria** & **Fluency Scoring Criteria** \\ \hline
0 & Negligible meaning is retained in the caption. & The generated caption is incomprehensible. \\
1 & Limited amount of meaning is retained. & The generated caption exhibits disfluency. \\
2 & Substantial portion of the meaning is retained. & The generated caption style appears non-native. \\
3 & Majority of the meaning is retained. & The generated caption is of good quality. \\
4 & Entirety of the meaning is retained. & The generated caption is flawless. \\
5 & The caption enhances the image with additional context. & The generated caption shows advanced mastery of language. \\ \hline \end{tabular}
\end{table}
Table 13: Scoring Criteria for Adequacy and Fluency.  
* **Consistency Across Sets:** The scores across the five sets are remarkably consistent for both Fluency and Adequacy. The Fluency scores ranged from 4.5 to 4.8, while the Adequacy scores fluctuated between 4.2 and 4.6. This consistency indicates a reliable performance by the DCAT model across different samples.
* **High Level of Fluency:** The Fluency scores are notably high, with an overall average of 4.7. This suggests that the captions generated by the model are grammatically correct, structurally coherent, and linguistically rich. The highest score of 4.8 in Set 4 implies near-flawless performance.
* **Adequate Context Capture:** The Adequacy scores, although slightly lower than Fluency, still hold an impressive average of 4.4. This signifies the model's capability in capturing the essential elements and context of each image. The score of 4.6 in Set 4 indicates exceptional performance in contextual understanding.
* **Incremental Improvement:** There is a noticeable incremental improvement from Set 1 to Set 4 for both metrics, perhaps indicating that the model benefits from a more diverse set of samples or improved tuning over iterations. However, Set 5 shows a minor decline, suggesting room for improvement.
* **Overall Performance:** The overall averages for Fluency and Adequacy are 4.7 and 4.4, respectively. These high scores substantiate the qualitative analysis, thereby confirming that the DCAT model is a reliable choice for image captioning, especially in low-resource languages like Tamil.  
These human evaluation results, combined with the quantitative analysis, affirm DCAT's effectiveness in generating high-quality image captions, highlighting its potential in applications involving low-resource languages like Tamil.