MUNIChus: Multilingual News Image Captioning Benchmark
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917332873379840 |
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| author | Chen, Yuji Plum, Alistair Hettiarachchi, Hansi Kanojia, Diptesh Basnet, Saroj Zampieri, Marcos Ranasinghe, Tharindu |
| author_facet | Chen, Yuji Plum, Alistair Hettiarachchi, Hansi Kanojia, Diptesh Basnet, Saroj Zampieri, Marcos Ranasinghe, Tharindu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_10613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MUNIChus: Multilingual News Image Captioning Benchmark Chen, Yuji Plum, Alistair Hettiarachchi, Hansi Kanojia, Diptesh Basnet, Saroj Zampieri, Marcos Ranasinghe, Tharindu Computation and Language Computer Vision and Pattern Recognition 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. |
| title | MUNIChus: Multilingual News Image Captioning Benchmark |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.10613 |