COCO-Urdu: A Large-Scale Urdu Image-Caption Dataset with Multimodal Quality Estimation
Paper • 2509.09014 • Published • 1
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End of preview. Expand in Data Studio
- Research Paper: https://www.arxiv.org/abs/2509.09014
- Github: https://github.com/umair-hassan2/COCO-Urdu
Overview
Urdu, spoken by over 250 million people, remains critically under-served in multimodal and vision-language research. COCO-Urdu addresses this gap by providing 59K images and 319K high-quality Urdu captions. Captions were generated via zero-shot translation using SeamlessM4T v2, validated with a hybrid QE pipeline combining COMET-Kiwi, CLIP-based visual grounding, and BERTScore with back-translation, and low-scoring captions were iteratively refined using open-source LLMs.
Consider citing the work if you find this helpful
@inproceedings{Hassan2025COCOUrduAL,
title={COCO-Urdu: A Large-Scale Urdu Image-Caption Dataset with Multimodal Quality Estimation},
author={Umair Hassan},
year={2025},
url={https://api.semanticscholar.org/CorpusID:281252320}
}
License Information
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