  Generalized Class Discovery (GCD) plays a pivotal role in discerning both
known and unknown categories from unlabeled datasets by harnessing the insights
derived from a labeled set comprising recognized classes. A significant
limitation in prevailing GCD methods is their presumption of an equitably
distributed category occurrence in unlabeled data. Contrary to this assumption,
visual classes in natural environments typically exhibit a long-tailed
distribution, with known or prevalent categories surfacing more frequently than
their rarer counterparts. Our research endeavors to bridge this disconnect by
focusing on the long-tailed Generalized Category Discovery (Long-tailed GCD)
paradigm, which echoes the innate imbalances of real-world unlabeled datasets.
In response to the unique challenges posed by Long-tailed GCD, we present a
robust methodology anchored in two strategic regularizations: (i) a reweighting
mechanism that bolsters the prominence of less-represented, tail-end
categories, and (ii) a class prior constraint that aligns with the anticipated
class distribution. Comprehensive experiments reveal that our proposed method
surpasses previous state-of-the-art GCD methods by achieving an improvement of
approximately 6 - 9% on ImageNet100 and competitive performance on CIFAR100.
