
Integrating multiple modalities, such as audio and visual data, can enable deep neural networks to learn more robust and generalizable representations, leading to improved performance and reduced catastrophic forgetting in continual learning scenarios.


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leveraging-multimodal-learning-to-mitigate-catastrophic-forgetting-in-continual-learning


Leveraging Multimodal Learning to Mitigate Catastrophic Forgetting in Continual Learning


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Multimodal continual learning (MMCL) seeks to enable AI systems to learn sequentially from diverse data streams (e.g., vision, language, audio) while retaining previously acquired knowledge, presenting unique challenges beyond unimodal continual learning.


a-comprehensive-survey-of-recent-advances-in-multimodal-continual-learning


A Comprehensive Survey of Recent Advances in Multimodal Continual Learning
