No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912107131305984 |
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| author | Mohamed, Youssef Li, Runjia Ahmad, Ibrahim Said Haydarov, Kilichbek Torr, Philip Church, Kenneth Ward Elhoseiny, Mohamed |
| author_facet | Mohamed, Youssef Li, Runjia Ahmad, Ibrahim Said Haydarov, Kilichbek Torr, Philip Church, Kenneth Ward Elhoseiny, Mohamed |
| contents | Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and Arabic). However we believe there should be more multilinguality. Hence, we present ArtELingo-28, a vision-language benchmark that spans $\textbf{28}$ languages and encompasses approximately $\textbf{200,000}$ annotations ($\textbf{140}$ annotations per image). Traditionally, vision research focused on unambiguous class labels, whereas ArtELingo-28 emphasizes diversity of opinions over languages and cultures. The challenge is to build machine learning systems that assign emotional captions to images. Baseline results will be presented for three novel conditions: Zero-Shot, Few-Shot and One-vs-All Zero-Shot. We find that cross-lingual transfer is more successful for culturally-related languages. Data and code are provided at www.artelingo.org. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages Mohamed, Youssef Li, Runjia Ahmad, Ibrahim Said Haydarov, Kilichbek Torr, Philip Church, Kenneth Ward Elhoseiny, Mohamed Computation and Language Artificial Intelligence Computers and Society Machine Learning Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and Arabic). However we believe there should be more multilinguality. Hence, we present ArtELingo-28, a vision-language benchmark that spans $\textbf{28}$ languages and encompasses approximately $\textbf{200,000}$ annotations ($\textbf{140}$ annotations per image). Traditionally, vision research focused on unambiguous class labels, whereas ArtELingo-28 emphasizes diversity of opinions over languages and cultures. The challenge is to build machine learning systems that assign emotional captions to images. Baseline results will be presented for three novel conditions: Zero-Shot, Few-Shot and One-vs-All Zero-Shot. We find that cross-lingual transfer is more successful for culturally-related languages. Data and code are provided at www.artelingo.org. |
| title | No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2411.03769 |