Generalized Categories Discovery for Long-tailed Recognition

• URL: http://arxiv.org/abs/2401.05352v2
• Date: Sun, 25 Aug 2024 09:58:25 GMT
• Title: Generalized Categories Discovery for Long-tailed Recognition
• Authors: Ziyun Li, Christoph Meinel, Haojin Yang,
• Abstract summary: Generalized Class Discovery plays a pivotal role in discerning both known and unknown categories from unlabeled datasets. Our research endeavors to bridge this disconnect by focusing on the long-tailed Generalized Category Discovery (Long-tailed GCD) paradigm. In response to the unique challenges posed by Long-tailed GCD, we present a robust methodology anchored in two strategic regularizations.
• Score: 8.69033435074757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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