NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts
This paper proposes NeuCME, a novel framework designed to effectively learn and integrate knowledge across tasks with varying modalities, and formulate an evaluation metric to quantify the dynamism of task sequences and then set up a comprehensive benchmark with different degrees of dynamism.
Kai Guo, Chuan-Bin Liu, Peng Hu et al.
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