Virtual Category-Guided Continual Generalized Category Discovery

Continual Generalized Category Discovery (C-GCD) aimsto incrementally identify novel categories from sequential unlabeled datawhile preserving recognition of known classes, which is an essential ca-pability for open-world visual learning. A major bottleneck lies in am-biguous unlabeled samples that cannot be confidently assigned to knownclasses nor reliably grouped as novel ones, making pseudo-labeling brit-tle and often biasing learning toward familiar categories. In this work,we introduce Virtual Category-Guided Continual Generalized CategoryDiscovery by adapting Virtual Category Learning (VCL) to the contin-ual setting. Our method identifies uncertain samples and assigns them totemporary virtual categories, enabling safe and informative learning fromunlabeled streams without injecting noisy labels, while improving unla-beled data utilization and mitigating prediction bias. To further stabilizediscovery across sessions and enhance class separation, we augment VCLwith Expanded Neighborhood Contrastive Learning (ENCL), which ex-ploits extended neighborhood relations and an adaptive margin to learnmore discriminative and well-separated representations for both old andemerging classes. Extensive experiments on CIFAR-100, Tiny ImageNet,and ImageNet-100 demonstrate that our approach consistently outper-forms state-of-the-art methods, establishing a scalable and effective so-lution for C-GCD. Code is on: https://github.com/Mrxjh105/VC-CGCD