# A Unified Visual and Linguistic Semantics Method for Enhanced Image Captioning
## 5 Experiments
### Performance Comparison
The CIDEr score of 164.2 is particularly noteworthy as it measures the consensus between the generated captions and a set of reference captions. This high score is indicative of the UVLS model's ability to produce unique and informative captions that are closely aligned with the semantic content of the images. When we shift our attention to the Flickr8k-CN and COCO-CN datasets, the UVLS model maintains its superior stance, with the highest scores across most metrics, particularly BLEU@1 and CIDEr, on the Flickr8k-CN dataset. The COCO-CN dataset, known for its complexity and diversity, also sees the UVLS model performing robustly, especially in the BLEU@1 and BLEU@2 scores, which are essential indicators of the model's consistency in capturing the essence of the visual data across diverse image sets. These consistent results across varied datasets highlight the UVLS model's robustness and its sophisticated approach to capturing not just the details but also the essence and narrative of the visual scenes. The model's capability to perform well in these key metrics indicates its deep learning ability to comprehend and integrate the visual and linguistic components effectively, suggesting its potential as a valuable method in the field of image captioning. The UVLS model's impressive 79.7% BLEU@1 score on the AIC-ICC dataset showcases its strength in generating coherent descriptions; however, this metric may not capture the full narrative depth or semantic richness of the captions. This suggests a need for critical examination of high BLEU scores as they may not always correlate with the quality of human-like narrative generation. Moreover, the progression to BLEU@2, BLEU@3, and BLEU@4 scores illustrates the model's ability to form more complex linguistic structures, yet it highlights the inherent limitation of these metrics in capturing the creative and idiomatic expressions found in human language. The top ROUGE score of 66.2% further indicates effective summarization, but this metric primarily measures keyword overlap, potentially overlooking subtler narrative elements or the emotional context depicted in images. Furthermore, the METEOR score, slightly lower than that of the 'Ours; Transformer' variant, prompts consideration of the model's capability to recognize synonyms and varied expressions, suggesting room for refinement to enhance naturalness and fluency in captions. While the CIDEr score of 164.2 showcases the model's ability to generate distinctive and informative captions, it also raises questions about the evaluation's emphasis on consensus over creativity. Across diverse datasets like Flickr8k-CN and COCO-CN, the UVLS model's consistent performance underscores its adaptability and depth in understanding visual content. However, this analysis invites further reflection on the model's handling of complex dataset diversity and its potential reliance on statistical regularities in training data, underscoring the importance of a nuanced approach to evaluating and advancing image captioning models. Table 2 reveals that the UVLS model significantly enhances the performance of image captioning, surpassing baseline models across the AIC-ICC, Flickr8k-CN, and COCO-CN datasets. It exhibits exceptional proficiency in the BLEU@1 metric, achieving 79.7% on the AIC-ICC dataset, which underlines its superior ability to recognize and utilize key words in captions--a critical element for crafting coherent and precise descriptions. This proficiency is further evidenced by an impressive BLEU@4 score of 50.7%, suggesting  
Table 2 reveals that the Unified Visual and Linguistic Semantics (UVLS) model significantly enhances the performance of image captioning, surpassing the baseline models across the AIC-ICC, Flickr8k-CN, and COCO-CN datasets. It exhibits exceptional proficiency in the BLEU@1 metric, achieving 79.7% on the AIC-ICC dataset, which underlines its superior ability to recognize and utilize key words in captions--a critical element for crafting coherent and precise descriptions.