mlsanityData to 7 Oct 2026

EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture

arXiv 2510.16198 ↗First posted 17 Oct 2025cs.CLcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 7 of its 20 references that are in the map. First posted on arXiv 17 Oct 2025.

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7 of 20 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Benchmarking Vision Language Models for Cultural Understanding15 Jul
  2. 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark10 Jun
  3. 2024CIC: A Framework for Culturally-Aware Image Captioning8 Feb
  4. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation28 Jan

Other references · 3

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,048
  3. 2020Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrievalcited by 305

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  1. 2025CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
  2. 2025Seeing Culture: A Benchmark for Visual Reasoning and Grounding
  3. 2025Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset
  4. 2025DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture
  5. 2026Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects
  6. 2024Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models
  7. 2024From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
  8. 2024M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks