Despite recent advances in AI, multimodal culturally diverse datasets are still limited, particularly for regions in the Middle East and Africa. In this paper, we introduce EgMM-Corpus, a multimodal dataset dedicated to Egyptian culture. By designing and running a new data collection pipeline, we collected over 3,000 images, covering 313 concepts across landmarks, food, and folklore. Each entry in the dataset is manually validated for cultural authenticity and multimodal coherence. EgMM-Corpus aims to provide a reliable resource for evaluating and training vision-language models in an Egyptian cultural context. We further evaluate the zero-shot performance of Contrastive Language-Image Pre-training CLIP on EgMM-Corpus, on which it achieves 21.2% Top-1 accuracy and 36.4% Top-5 accuracy in classification. These results underscore the existing cultural bias in large-scale vision-language models and demonstrate the importance of EgMM-Corpus as a benchmark for developing culturally aware models.
@article{arxiv.2510.16198,
title = {EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture},
author = {Mohamed Gamil and Abdelrahman Elsayed and Abdelrahman Lila and Ahmed Gad and Hesham Abdelgawad and Mohamed Aref and Ahmed Fares},
journal= {arXiv preprint arXiv:2510.16198},
year = {2025}
}