AIBullisharXiv – CS AI · Mar 37/107
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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.