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arXiv · 2510.17405

AFRICAPTION: Establishing a New Paradigm for Image Captioning in African Languages

Mardiyyah Oduwole·Prince Mireku·Fatimo Adebanjo·Oluwatosin Olajide·Mahi Aminu Aliyu·Jekaterina Novikova
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Abstract

Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantically aligned captions generated via a context-aware selection and translation process; (ii) a dynamic, context-preserving pipeline that ensures ongoing quality through model ensembling and adaptive substitution; and (iii) the AfriCaption model, a 0.5B parameter vision-to-text architecture that integrates SigLIP and NLLB200 for caption generation across under-represented languages. This unified framework ensures ongoing data quality and establishes the first scalable image-captioning resource for under-represented African languages, laying the groundwork for truly inclusive multimodal AI.

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Mardiyyah Oduwole, Prince Mireku, Fatimo Adebanjo, Oluwatosin Olajide, Mahi Aminu Aliyu, Jekaterina Novikova. 2025-10-20. AFRICAPTION: Establishing a New Paradigm for Image Captioning in African Languages. https://arxiv.org/abs/2510.17405

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