DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning

Researchers propose DeLo, a new framework using dual-decomposed low-rank expert architecture to help Large Multimodal Models adapt to real-world scenarios with incomplete data. The system addresses continual missing modality learning by preventing interference between different data types and tasks through specialized routing and memory mechanisms.