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About this paper
Xianyuan Liu, Jiayang Zhang, Shuo Zhou, Thijs L. van der Plas, Avish Vijayaraghavan, Anastasiia Grishina, Mengdie Zhuang, Daniel Schofield and 40 more
Researchers from the University of Sheffield and The Alan Turing Institute, along with a broad international collaboration, propose a deployment-centric workflow for multimodal AI development. This framework aims to broaden the field beyond its current vision and language focus by integrating real-world constraints and diverse data modalities to create more practical and impactful AI solutions.
Key insights
- Current multimodal AI research exhibits a strong bias towards vision and language, with an analysis of arXiv preprints showing over 50% of studies in these modalities.
- Effective real-world multimodal AI deployment requires addressing five distinct challenges: modality incompleteness, heterogeneity, cross-modality alignment, complementarity, and privacy risk.
- A comprehensive understanding of multimodality, encompassing various data types, subtypes, views, and fidelities, is crucial for developing impactful AI solutions across disciplines.
Summary by alphaXiv.
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