Arabic Multimodal Machine Learning: Datasets, Applications, Approaches, and Challenges
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Abstract (arXiv)
Multimodal Machine Learning (MML) aims to integrate and analyze information from diverse modalities, such as text, audio, and visuals, enabling machines to address complex tasks like sentiment analysis, emotion recognition, and multimedia retrieval. Recently, Arabic MML has reached a certain level of maturity in its foundational development, making it time to conduct a comprehensive survey. This paper explores Arabic MML by categorizing efforts through a novel taxonomy and analyzing existing research. Our taxonomy organizes these efforts into four key topics: datasets, applications, approaches, and challenges. By providing a structured overview, this survey offers insights into the current state of Arabic MML, highlighting areas that have not been investigated and critical research gaps. Researchers will be empowered to build upon the identified opportunities and address challenges to advance the field.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 64 references that are in the map. First posted on arXiv 17 Aug 2025.
Builds on
17 of 64 references.
References · 17
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5 more
- 2025AIN: The Arabic INclusive Large Multimodal Modelcited by 8
- 2024Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognitioncited by 7
- 2022Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigmcited by 3
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Citing papers per year: 2025: 1.
1 paper.