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arXiv Machine Learning By Salimeh Sekeh, Mary Wisell

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

Read the original on arXiv Machine Learning →

arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.

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