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MUNIChus: Multilingual News Image Captioning Benchmark

arXiv 2603.10613 ↗First posted 11 Mar 2026cs.CL cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 11 Mar 2026.

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25 of 41 references.

References · 25

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13 more
  1. 2020Transform and Tell: Entity-Aware News Image Captioningcited by 52
  2. 2019Survey on Publicly Available Sinhala Natural Language Processing Tools and Researchcited by 45
  3. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  4. 2022Deep Learning Approaches on Image Captioning: A Reviewcited by 43
  5. 2021CIDEr-R: Robust Consensus-based Image Description Evaluationcited by 22
  6. 2024A Survey of Multimodal Sarcasm Detectioncited by 19
  7. 2023LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Promptingcited by 17
  8. 2023Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignmentcited by 15
  9. 2023Visually-Aware Context Modeling for News Image Captioningcited by 12
  10. 2023"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioningcited by 7
  11. 2024NSINA: A News Corpus for Sinhalacited by 6
  12. 2025Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detectioncited by 3
  13. 2020Towards Annotation-Free Evaluation of Cross-Lingual Image Captioningcited by 2

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