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Regularizing modality contribution drift in multimodal continual learning
Multimodal continual learning (MMCL) aims to acquire new knowledge from multimodal data while retaining previously learned knowledge. Existing MMCL methods primarily mitigate forgetting by aligning cross-modal representations or preserving feature-level semantic similarity. However, different tasks
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