In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models, we would explore replacing discriminative models with generative ones for CIL. However, transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally, it requires formulating the task of CIL within a generative framework. To this end, we propose a novel generative multi-modal model (GMM) framework for class-incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text, we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings, we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting, we have improved by at least 14\% accuracy over all the current state-of-the-art methods with significantly less forgetting. Our code is available at \url{https://github.com/DoubleClass/GMM}.

翻译：在类增量学习（CIL）场景中，分类器因对当前任务产生偏置而导致的灾难性遗忘现象长期以来一直是重大挑战，这主要归因于判别式模型的特性。随着生成式多模态模型的日益普及，我们探索用生成式模型替代判别式模型来解决CIL问题。然而，从判别式模型转向生成式模型需要应对两个关键挑战。首要挑战在于将生成的文本信息转化为对不同类别的分类任务；其次需要将CIL任务形式化地嵌入生成式框架中。为此，我们提出了一种面向类增量学习的新型生成式多模态模型（GMM）框架。该方法直接利用适配后的生成模型为图像生成标签，在获取详细文本后，使用文本编码器提取文本特征，并通过特征匹配确定最相似标签作为分类预测结果。在传统CIL设定下，我们在长序列任务场景中取得了显著更优的结果；在少样本CIL设定下，我们的方法相较现有最先进方法在准确率上提升至少14%，且遗忘程度显著降低。我们的代码已开源在 \url{https://github.com/DoubleClass/GMM}。