Data augmentation in multimodal frameworks: a survey

Training machine learning models with more than one data modality has enhanced predictive performance in most contexts. Thus, many recent applications of machine learning use data from different sources and forms. Multimodal data augmentation (MMDA) addresses critical challenges in multimodal learning, such as data scarcity, modality imbalance, and cross-modal alignment. This survey systematically reviews 68 state-of-the-art MMDA approaches, and, as result, proposes a taxonomy for the area. For

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ethics.ai (14 August 2026), “Data augmentation in multimodal frameworks: a survey,” evidence record 19546, https://ethics.ai/record/19546 (originally published by Artificial Intelligence Review).

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