Incremental Prototype Prompt-tuning with Pre-trained Representation for Class Incremental Learning

Class incremental learning has attracted much attention, but most existing works still continually fine-tune the representation model, resulting in much catastrophic forgetting. Instead of struggling to fight against such forgetting by replaying or distillation like most of the existing methods, we take the pre-train-and-prompt-tuning paradigm to sequentially learn new visual concepts based on a fixed semantic rich pre-trained representation model by incremental prototype prompt-tuning (IPP), which substantially reduces the catastrophic forgetting. In addition, an example prototype classification is proposed to compensate for semantic drift, the problem caused by learning bias at different phases. Extensive experiments conducted on the three incremental learning benchmarks demonstrate that our method consistently outperforms other state-of-the-art methods with a large margin.