<ul><li>Multi-modal learning has achieved remarkable success in integrating information from various modalities and outperforming uni-modal approaches in tasks like recognition and retrieval.</li><li>However, current methods struggle to address the challenge of generalizing to novel modalities that are unseen during training.</li><li>This paper introduces Modality Generalization (MG) to enable models to generalize to unseen modalities.</li><li>The authors propose a benchmark and identify key directions for future research to advance robust and adaptable multi-modal models.</li></ul>

Towards Modality Generalization: A Benchmark and Prospective Analysis

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