Multimodal models trained on complete modality data often exhibit a
substantial decrease in performance when faced with imperfect data containing
corruptions or missing modalities. To address this robustness challenge, prior
methods have explored various approaches from aspects of augmentation,
consistency or uncertainty, but these approaches come with associated drawbacks
related to data complexity, representation, and learning, potentially
diminishing their overall effectiveness. In response to these challenges, this
study introduces a novel approach known as the Redundancy-Adaptive Multimodal
Learning (RAML). RAML efficiently harnesses information redundancy across
multiple modalities to combat the issues posed by imperfect data while
remaining compatible with the complete modality. Specifically, RAML achieves
redundancy-lossless information extraction through separate unimodal
discriminative tasks and enforces a proper norm constraint on each unimodal
feature representation. Furthermore, RAML explicitly enhances multimodal fusion
by leveraging fine-grained redundancy among unimodal features to learn
correspondences between corrupted and untainted information. Extensive
experiments on various benchmark datasets under diverse conditions have
consistently demonstrated that RAML outperforms state-of-the-art methods by a
significant margin