Year: 2026
Type: conference-paper
Abstract: Image captioning plays a crucial role in numerous applications, including educational systems.However, ensuring caption quality remains a significant challenge, particularly for morphologically rich, low-resource languages such as Arabic.We investigate an evaluation of Arabic image captioning using state-of-the-art multimodal foundation models.We systematically assess the performance of leading models-Gemini, Gemma, LLaMA, and Fanar.Our evaluation framework employs a diverse set of metrics spanning rule-based, learnable, visually-grounded, and LLM-based approaches to capture semantic accuracy, linguistic fluency, and hallucination detection.Experiments are conducted on two benchmark datasets: Flickr8k-Arabic and JEEM.Our findings reveal significant performance variations across models and evaluation metrics, highlighting the need for Arabic-specific optimization in multimodal architectures.
Institution: Amar Telidji University of Laghouat
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166531065