Benchmarking large vision language models for multilingual image caption generation in low resource settings

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The problem of generating image captions for low resource, right to left languages like Urdu and Arabic has not been a well explored problem in the field of multimodal learning. There is a huge gap in multilingual visual understanding as existing captioning systems are mostly developed to cater for English language. This paper introduces the multi-lingual image caption generation system to generate semantically aligned Urdu and Arabic captioning for a single image. The proposed framework generates bilingual caption descriptions by employing a vision-language (Vis-Lng) pipeline that requires no pivot-translation step at inference; the underlying bilingual training data is itself translation-derived (Sect. 3). The ZARWA-v1 corpus introduced here were constructed by translating English source captions into Urdu and Arabic. We perform a comprehensive evaluation of different multimodal models with multilingual capabilities, such as Qwen2.5-VL, Pangea, PALO, mBLIP, PaliGemma2 and LLaVA-OV, together with three additional English-centric vision-language models (Florence-2, InstructBLIP, BLIP-2) used as baselines, using zero-shot, LoRA and QLoRA fine-tuning settings. The fine-tuning process with LoRA and QLoRA further enhances the quality of captions in both languages, with Qwen2.5-VL showing the best performance when fine-tuned with QLoRA, resulting in a BLEU-4 score of 48.5 for Urdu and 50.1 for Arabic. A set of automatic evaluation metrics such as BLEU, ROUGE-L, METEOR, chrF, BERTScore, LASER, and LaBSE, and human evaluation on 5 criteria are applied. Results show that QLoRA fine-tuning can achieve competitive or better performance than full LoRA adaptation with significantly reduced computational resources. Cross-lingual semantic consistency analysis further confirms that the generated Urdu and Arabic captions remain semantically aligned across all fine-tuned models.