Dynamic Routing Between Experts: A Data-Efficient Approach to Continual Learning in Vision-Language Models

A new study introduces a dynamic routing approach to improve continual learning in vision-language models, addressing the issue of catastrophic forgetting. This method allows models to learn new tasks without losing previously acquired knowledge, making it a significant advancement in the field. By reducing the need for simultaneous access to all datasets, it also lessens computational demands, which is crucial for practical applications. This innovation could enhance the efficiency and effectiveness of AI systems in understanding and processing language and visual data.