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MUNIChus: Multilingual News Image Captioning Benchmark

arXiv cs.CL / 3/12/2026

📰 NewsModels & Research
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Key Points

  • MUNIChus is introduced as the first multilingual benchmark for news image captioning, spanning 9 languages including Sinhala and Urdu.
  • The dataset addresses the shortage of multilingual resources in this field and enables cross-lingual evaluation.
  • The benchmark evaluates several state-of-the-art neural models and confirms that multilingual news image captioning remains challenging.
  • The authors publicly release MUNIChus with benchmarking results for over 20 models, facilitating further research and benchmarking.
  • This release opens new avenues for advancing multilingual news image captioning research and its evaluation.

Abstract

The goal of news image captioning is to generate captions by integrating news article content with corresponding images, highlighting the relationship between textual context and visual elements. The majority of research on news image captioning focuses on English, primarily because datasets in other languages are scarce. To address this limitation, we create the first multilingual news image captioning benchmark, MUNIChus, comprising 9 languages, including several low-resource languages such as Sinhala and Urdu. We evaluate various state-of-the-art neural news image captioning models on MUNIChus and find that news image captioning remains challenging. We also make MUNIChus publicly available with over 20 models already benchmarked. MUNIChus opens new avenues for further advancements in developing and evaluating multilingual news image captioning models.

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