Regularizing modality contribution drift in multimodal continual learning

תקציר מקורי באנגליתarXiv:2607.27260v2 Announce Type: replace Abstract: 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 may rely on different modalities, and learning new tasks can alter how modalities contribute to predictions on previously learned tasks. It remains underexplored how modality contributions evolve across incremental stages and how such changes relate to forgetting in MMCL. We term such changes Modality Contribution Drift (MCD) and introduce an MCD score based on controlled modality-subset interventions. Our theoretical and empirical analyses sho