  With the emergence of Transformers and Vision-Language Models (VLMs) such as
CLIP, large pre-trained models have become a common strategy to enhance
performance in Continual Learning scenarios. This led to the development of
numerous prompting strategies to effectively fine-tune transformer-based models
without succumbing to catastrophic forgetting. However, these methods struggle
to specialize the model on domains significantly deviating from the
pre-training and preserving its zero-shot capabilities. In this work, we
propose Continual Generative training for Incremental prompt-Learning, a novel
approach to mitigate forgetting while adapting a VLM, which exploits generative
replay to align prompts to tasks. We also introduce a new metric to evaluate
zero-shot capabilities within CL benchmarks. Through extensive experiments on
different domains, we demonstrate the effectiveness of our framework in
adapting to new tasks while improving zero-shot capabilities. Further analysis
reveals that our approach can bridge the gap with joint prompt tuning. The
codebase is available at https://github.com/aimagelab/mammoth.
