  Continual learning (CL) has attracted increasing attention in the recent
past. It aims to mimic the human ability to learn new concepts without
catastrophic forgetting. While existing CL methods accomplish this to some
extent, they are still prone to semantic drift of the learned feature space.
Foundation models, which are endowed with a robust feature representation,
learned from very large datasets, provide an interesting substrate for the
solution of the CL problem. Recent work has also shown that they can be adapted
to specific tasks by prompt tuning techniques that leave the generality of the
representation mostly unscathed. An open question is, however, how to learn
both prompts that are task specific and prompts that are global, i.e. capture
cross-task information. In this work, we propose the Prompt Of Prompts (POP)
model, which addresses this goal by progressively learning a group of
task-specified prompts and a group of global prompts, denoted as POP, to
integrate information from the former. We show that a foundation model equipped
with POP learning is able to outperform classic CL methods by a significant
margin. Moreover, as prompt tuning only requires a small set of training
samples, POP is able to perform CL in the few-shot setting, while still
outperforming competing methods trained on the entire dataset.
