Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

Cross-lingual image description, the task of generating image captions in a target language from images and descriptions in a source language, is addressed in this study through a novel approach that combines neural network models and semantic matching techniques. Experiments conducted on the Flickr8k and AraImg2k benchmark datasets, featuring images and descriptions in English and Arabic, showcase remarkable performance improvements over state-of-the-art methods. Our model, equipped with the Image & Cross-Language Semantic Matching module and the Target Language Domain Evaluation module, significantly enhances the semantic relevance of generated image descriptions. For English-to-Arabic and Arabic-to-English cross-language image descriptions, our approach achieves a CIDEr score for English and Arabic of 87.9% and 81.7%, respectively, emphasizing the substantial contributions of our methodology. Comparative analyses with previous works further affirm the superior performance of our approach, and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language. In summary, this study advances the field of cross-lingual image description, offering an effective solution for generating image captions across languages, with the potential to impact multilingual communication and accessibility. Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.