Summary of No Culture Left Behind: Artelingo-28, a Benchmark Of Wikiart with Captions in 28 Languages, by Youssef Mohamed et al.
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
by Youssef Mohamed, Runjia Li, Ibrahim Said Ahmad, Kilichbek Haydarov, Philip Torr, Kenneth Ward Church, Mohamed Elhoseiny
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ArtELingo-28 benchmark challenges machine learning systems to assign emotional captions to images across 28 languages, with approximately 200,000 annotations (140 per image). This vision-language task emphasizes diversity of opinions over languages and cultures. The goal is to build models that can transfer knowledge from one language to another, particularly for culturally-related languages. The paper presents baseline results for three novel conditions: Zero-Shot, Few-Shot, and One-vs-All Zero-Shot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where machines can understand emotions in images across many languages! A new benchmark called ArtELingo-28 helps machines learn to assign emotional captions to pictures in 28 different languages. This is important because it shows how well machines can transfer knowledge from one language to another, especially when cultures are similar. |
Keywords
* Artificial intelligence * Few shot * Machine learning * Zero shot
