# A Unified Visual and Linguistic Semantics Method for Enhanced Image Captioning
## 5 Experiments
### Performance Comparison  
The comparative examination of image captioning models, as illustrated in Table 2, highlights the superior performance of the Unified Visual and Linguistic Semantics Method (UVLS) across various evaluation metrics. The detailed data provided in Table 2 enable in-depth analysis of the Unified Visual and Linguistic Semantics (UVLS) model's performance relative to other contemporary image captioning models across three datasets: AIC-ICC, Flickr8k-CN, and COCO-CN. Starting with the AIC-ICC dataset, the UVLS model's performance can be characterized by its BLEU@1 score of 79.7%, which surpasses other models significantly. This high score indicates that the UVLS model is particularly adept at correctly identifying and employing the most frequent words in captions, which is fundamental for establishing a base for accurate and relevant descriptions. As we progress to BLEU@2 and BLEU@3 scores, where scores of 68.9% and 59.1%, respectively, are observed, there is a clear indication of the model's ability to construct longer and more complex linguistic structures. This is critical for forming coherent and detailed narratives that are necessary for a complete understand \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Model** & **BLEU@1** & **BLEU@2** & **BLEU@3** & **BLEU@4** & **METEOR** & **ROUGE** & **CIDEr** \\ \hline \multirow{6}{*}{AIC-ICC} & CS-NIC & 62.8 & 48.0 & 37.0 & 28.7 & 30.7 & 51.8 & 74.6 \\ \cline{2-9}  & CIC & 66.3 & 52.5 & 41.7 & 33.2 & 33.3 & 56.5 & 92.3 \\ \cline{2-9}  & SC & 73.0 & 60.3 & 49.6 & 41.0 & 35.3 & 60.3 & 116.6 \\ \cline{2-9}  & Oscar & 74.4 & 65.1 & 50.6 & 43.1 & 36.5 & 62.4 & 140.5 \\ \cline{2-9}  & CLIPCAP & 76.7 & 65.1 & 55.0 & 46.4 & 38.8 & 64.0 & 151.2 \\ \cline{2-9}  & Ours; UVLS & **79.7** & **68.9** & **59.1** & **50.7** & **40.1** & **66.2** & **164.2** \\ \hline \multirow{6}{*}{Flickr8k-CN} & CS-NIC & 63.2 & 50.2 & 38.4 & 28.1 & 31 & 54.7 & 81.6 \\ \cline{2-9}  & CIC & 68.7 & 55.5 & 43.3 & 31.6 & 33.2 & 59 & 96 \\ \cline{2-9}  & SC & 73.3 & 59.7 & 47.3 & 35.9 & 35.3 & 61 & 121.3 \\ \cline{2-9}  & Oscar & 80.4 & 69.5 & 59.4 & 50 & 41.2 & 67.3 & 160.5 \\ \cline{2-9}  & CLIPCAP & 84.4 & 72.3 & 62.1 & 52 & 43.9 & 70.3 & 170.2 \\ \cline{2-9}  & Ours; UVLS & **90.7** & **75.9** & **67.1** & **56.7** & **48.1** & **76.2** & **184.2** \\ \hline \multirow{6}{*}{COCO-CN} & CS-NIC & 42.7 & 31.6 & 16.9 & 10.5 & 5.6 & 24.9 & 50.3 \\ \cline{2-9}  & CIC & 50.1 & 42.2 & 27.6 & 20.6 & 7.3 & 26.9 & 65.9 \\ \cline{1-1} \cline{2-9}  & SC & 59.5 & 42.3 & 35.7 & 24.6 & 10.6 & 30.9 & 80.8 \\ \cline{1-1} \cline{2-9}  & Oscar & 69.4 & 58.3 & 48.6 & 38.5 & 20.4 & 46.3 & 107.8 \\ \cline{1-1} \cline{2-9}  & CLIPCAP & 71.8 & 61.2 & 50.9 & 41.4 & 25.4 & 55.7 & 110.4 \\ \cline{1-1} \cline{2-9}  & Ours; UVLS & **74.7** & **64.8** & **56.0** & **49.3** & **30.6** & **58.4** & **124.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of evaluation indexes of different models on ICC dataset. Top scores for each metric are bold. All values are reported as percentage (%). ing of an image. The BLEU@4 score of 50.7% further solidifies UVLS's strength in creating extended phrases and sentences that are syntactically varied and contextually rich. The model's top ROUGE score of 66.2% on the AIC-ICC dataset reflects its superior summarization capability, suggesting that it can capture the essential content of images in a way that is most similar to the reference captions. This is particularly important in image captioning as it ensures that the generated captions are not only descriptive but also encapsulate the main themes and messages conveyed by the image. In terms of the METEOR metric, which evaluates the harmony between the generated captions and reference captions in terms of both precision and recall, the UVLS model scores a 40.1%. Although this is slightly lower compared to the 'Ours; Transformer' variant, it is worth noting that METEOR also considers synonymy and paraphrasing, which could suggest areas for further refinement in the UVLS model.