  We tackle the problem of Continual Category Discovery (CCD), which aims to
automatically discover novel categories in a continuous stream of unlabeled
data while mitigating the challenge of catastrophic forgetting -- an open
problem that persists even in conventional, fully supervised continual
learning. To address this challenge, we propose PromptCCD, a simple yet
effective framework that utilizes a Gaussian Mixture Model (GMM) as a prompting
method for CCD. At the core of PromptCCD lies the Gaussian Mixture Prompting
(GMP) module, which acts as a dynamic pool that updates over time to facilitate
representation learning and prevent forgetting during category discovery.
Moreover, GMP enables on-the-fly estimation of category numbers, allowing
PromptCCD to discover categories in unlabeled data without prior knowledge of
the category numbers. We extend the standard evaluation metric for Generalized
Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on
diverse public datasets. PromptCCD significantly outperforms existing methods,
demonstrating its effectiveness. Project page:
https://visual-ai.github.io/promptccd .
