VPT-NSP²++: Importance-Aware Visual Prompt Tuning in Null Space for Continual Learning

Continual learning (CL) enables AI models to adapt to evolving environments while mitigating catastrophic forgetting, which is a critical capability for dynamic real-world applications. With the growing popularity of pre-trained Vision Transformer (ViT) models and visual prompt tuning (VPT) technique in CL, this work explores a CL method on top of the ViT-based foundation model, through VPT mechanism with theoretical guarantees. Inspired by the orthogonal projection method, we aim to leverage this approach for VPT to enhance CL performance, particularly in long-term scenarios. However, since the orthogonal projection is originally designed for linear operations in CNNs, applying it to ViTs poses challenges induced by the non-linear self-attention mechanism and the distribution drift within LayerNorm. To address these issues, we deduced two orthogonality conditions to achieve the prompt gradient orthogonal projection, which provide a theoretical guarantee of maintaining stability. Considering the strict orthogonal constraints can diminish model capacity and reduce plasticity, we further propose an importance-aware orthogonal regularization framework. By applying varying degrees of orthogonal constraints to different parameters based on their importance to old and new tasks, the framework adaptively enhances model capacity and thereby promotes long-sequence CL while improving the stability-plasticity trade-off. To implement the proposed approach, a null-space-based approximation solution is employed to efficiently achieve the prompt gradient orthogonal projection. Extensive experiments on various class-incremental learning benchmarks demonstrate that our method achieves state-of-the-art performance across diverse CL scenarios.