## 5 Experimentation, Results, and Analysis
### Human Evaluation
#### 5.4.8 Cross-Modal Retrieval Analysis  
To further validate the quality of multimodal alignment in the DCAT model, we incorporate a cross-modal retrieval task as recommended. Specifically, we evaluate the image-to-text and text-to-image retrieval accuracy on the Flickr8k and Flickr30k datasets. For this purpose, we employ standard retrieval metrics: Recall@K (R@1, R@5, and R@10), which measures the percentage of times the correct caption (or image) is among the top K retrieved results.  
Evaluation Protocol  
Each image is associated with five ground truth captions. For image-to-text retrieval, we retrieve captions given an image query. For text-to-image retrieval, we retrieve images using a caption query. We compute cosine similarity between the projected embeddings from both modalities. The results of the cross-modal retrieval evaluation are reported in Table 17.  
As observed, DCAT achieves superior Recall@K scores for both retrieval directions, across both datasets. Notably, DCAT surpasses AoANet by approximately 6% at R@1 for  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Baseline Model** & **Mean CES (DCAT)** & **Mean CES (Baseline)** & _p_**-Value** \\ \hline NIC & 0.6246 & 0.4656 & \textless{}0.01 \\ S2 VT & 0.6246 & 0.4921 & \textless{}0.01 \\ Up-Down Attention & 0.6246 & 0.5216 & \textless{}0.01 \\ Image Transformer & 0.6246 & 0.5386 & \textless{}0.01 \\ Oscar & 0.6246 & 0.5630 & \textless{}0.05 \\ AoANet & 0.6246 & 0.5916 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 16: _t_-test results for comparing dcat with baseline models.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Image\(\rightarrow\)Text (R@K)**} & \multicolumn{3}{c}{**Text\(\rightarrow\)Image (R@K)**} \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline \multirow{2}{*}{DCAT (Proposed)} & Flickr8k & 62.4 & 85.3 & 94.0 & 59.8 & 83.1 & 92.6 \\  & Flickr30k & 61.1 & 84.5 & 93.5 & 58.2 & 81.9 & 91.4 \\ \hline \multirow{2}{*}{NIC} & Flickr8k & 39.5 & 66.2 & 79.7 & 36.8 & 63.9 & 77.2 \\  & Flickr30k & 38.1 & 65.1 & 78.4 & 35.7 & 62.7 & 76.3 \\ \hline \multirow{2}{*}{S2 VT} & Flickr8k & 41.7 & 68.3 & 81.0 & 39.5 & 66.8 & 79.4 \\  & Flickr30k & 40.9 & 67.2 & 80.1 & 38.7 & 65.2 & 78.2 \\ \hline \multirow{2}{*}{Up-Down} & Flickr8k & 45.3 & 71.4 & 83.5 & 42.9 & 69.5 & 81.6 \\  & Flickr30k & 44.2 & 70.1 & 82.3 & 41.6 & 68.0 & 80.4 \\ \hline \multirow{2}{*}{Image Transformer} & Flickr8k & 48.6 & 73.8 & 85.6 & 46.0 & 71.5 & 83.3 \\  & Flickr30k & 47.1 & 72.4 & 84.5 & 44.7 & 70.0 & 82.1 \\ \hline \multirow{2}{*}{Oscar} & Flickr8k & 52.1 & 77.2 & 89.0 & 49.6 & 75.1 & 87.1 \\  & Flickr30k & 50.9 & 75.8 & 88.3 & 48.1 & 73.6 & 86.2 \\ \hline \multirow{2}{*}{AoANet} & Flickr8k & 56.8 & 80.9 & 91.5 & 53.9 & 79.0 & 89.4 \\  & Flickr30k & 55.2 & 79.3 & 90.4 & 52.3 & 77.1 & 88.3 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Cross-modal retrieval performance (Recall@K) on Flickr8k and Flickr30k.  
image-to-text retrieval, indicating more precise visual-language alignment. The results confirm that the dynamic context modeling and multimodal fusion strategy of DCAT enhances bidirectional retrieval, thereby supporting the model's effectiveness in aligning visual and textual modalities.