2026
Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting
CVPR 2026
We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge using parameter-efficient methods, such as prefix-tuning or adapters, which facilitate model adaptation to downstream tasks by incorporating task-spec