General Incomplete Multimodal Learning via Dynamic Quality Perception

Multimodal learning robust to missing modalities is essen-tial for real-world applications. Existing methods mainly focus on inter-modality missing, where entire modalities are absent, while overlookingintra-modality degradation, where modalities are present but severelycorrupted. In practice, these two types of missing often coexist, mak-ing existing approaches ineffective. To address this limitation, we pro-pose General Incomplete Multimodal Learning (GIML), a unified frame-work that simultaneously handles both inter-modality missing and intra-modality degradation through dynamic quality perception. Specifically,GIML models heterogeneous missing patterns as continuous modality in-formation degradation, enabling degradation-aware adaptive fusion. Toachieve reliable quality perception, we introduce a Noise-aware QualityEstimator that learns the mapping from corrupted features to noise in-tensity through controlled noise injection. Furthermore, we propose aNoise–Semantic Decoupled module that separates semantic informationfrom noise interference. This improves robustness and generalization tounseen corruption patterns. Extensive experiments across datasets withdiverse modality types demonstrate the effectiveness and generality ofGIML. Code is available at: https://github.com/Yu-Five/GIML.