This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
Simmlm: A simple framework for multi-modal learning with missing modality.arXiv preprint arXiv:2507.19264, 2025b
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Complex multimodal architectures do not reliably outperform unimodal baselines or a simple multimodal baseline under standardized evaluation.
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.
citing papers explorer
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Deep Multimodal Learning with Missing Modality: A Survey
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
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Fusion or Confusion? Multimodal Complexity Is Not All You Need
Complex multimodal architectures do not reliably outperform unimodal baselines or a simple multimodal baseline under standardized evaluation.
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Calibrated Multimodal Representation Learning with Missing Modalities
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.