  Drawing inspiration from prompt tuning techniques applied to Large Language
Models, recent methods based on pre-trained ViT networks have achieved
remarkable results in the field of Continual Learning. Specifically, these
approaches propose to maintain a set of prompts and allocate a subset of them
to learn each task using a key-query matching strategy. However, they may
encounter limitations when lacking control over the correlations between old
task queries and keys of future tasks, the shift of features in the latent
space, and the relative separation of latent vectors learned in independent
tasks. In this work, we introduce a novel key-query learning strategy based on
orthogonal projection, inspired by model-agnostic meta-learning, to enhance
prompt matching efficiency and address the challenge of shifting features.
Furthermore, we introduce a One-Versus-All (OVA) prototype-based component that
enhances the classification head distinction. Experimental results on benchmark
datasets demonstrate that our method empowers the model to achieve results
surpassing those of current state-of-the-art approaches by a large margin of up
to 20%.
