Abstract
During multimodal model training and testing, certain data modalities may be
absent due to sensor limitations, cost constraints, privacy concerns, or data
loss, negatively affecting performance. Multimodal learning techniques designed
to handle missing modalities can mitigate this by ensuring model robustness
even when some modalities are unavailable. This survey reviews recent progress
in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning
methods. It provides the first comprehensive survey that covers the motivation
and distinctions between MLMM and standard multimodal learning setups, followed
by a detailed analysis of current methods, applications, and datasets,
concluding with challenges and future directions.