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%.

翻译：受大语言模型提示调优技术的启发，基于预训练ViT网络的近期方法在连续学习领域取得了显著成果。具体而言，这些方法通过维护一组提示，并利用键-查询匹配策略为每个任务分配子集进行学习。然而，当缺乏对旧任务查询与未来任务键之间相关性的控制、潜在空间中特征的偏移现象，以及独立任务所学习潜在向量的相对分离性时，这些方法可能面临局限性。本文提出了一种基于正交投影的新型键-查询学习策略——受模型无关元学习启发，通过增强提示匹配效率并应对特征偏移挑战。此外，我们引入基于原型（One-Versus-All, OVA）的组件以提升分类头的区分能力。在基准数据集上的实验结果表明，本方法使模型性能大幅超越现有最优方法，最高提升幅度达20%。