Mohamed Elhoseiny


ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture
Youssef Mohamed | Mohamed Abdelfattah | Shyma Alhuwaider | Feifan Li | Xiangliang Zhang | Kenneth Church | Mohamed Elhoseiny
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K in Spanish to evaluate “cultural-transfer” performance. 51K artworks have 5 annotations or more in 3 languages. This diversity makes it possible to study similarities and differences across languages and cultures. Further, we investigate captioning tasks, and find diversity improves the performance of baseline models. ArtELingo is publicly available at ‘‘ with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.


pdf bib
Automatic Annotation of Structured Facts in Images
Mohamed Elhoseiny | Scott Cohen | Walter Chang | Brian Price | Ahmed Elgammal
Proceedings of the 5th Workshop on Vision and Language


Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions
Mohamed Elhoseiny | Ahmed Elgammal
Proceedings of the Fourth Workshop on Vision and Language