Naming to Learn: Class Incremental Learning for Vision-Language Model with Unlabeled Data

Class Incremental Learning (CIL) enables models to adapt to evolving data distributions by learning new classes over time without revisiting previous data. While recent methods utilizing pre-trained models have shown promising results, they often assume access to fully labeled data for each incremental task, which is often impractical. In this paper, we instead tackle a more realistic scenario in which only unlabeled data and the class-name set are available for each new class. Although one could generate pseudo labels with a vision-language model and apply existing CIL methods, the inevitable noise in these pseudo labels tends to aggravate catastrophic forgetting. To overcome this challenge, we propose a method named N2L employing a regression objective with mean squared error loss, which can be solved in a recursive manner. To refine the pseudo labels, N2L applies feature dimensionality reduction to the extracted image features and iteratively updates the labels using a classifier trained on these reduced features. Furthermore, a bi-level weight adjustment strategy is proposed to downweight low-confidence pseudo labels via intra-class adjustment and compensate for pseudo-label class imbalance through inter-class adjustment. This incremental learning with adjustment can be solved recursively, yielding identical performance to joint training with unlabeled data and thereby mitigating forgetting. Our theoretical analysis supports the effectiveness of the pseudo label refinement process, and experiments on various datasets demonstrate that our proposed method outperforms SOTA methods. Code is available at https://github.com/zhoujiahuan1991/ICLR2026-N2L