Clustering-Based Tail-class Mitigation for New-class Discovery

摘要

Open-world semi-supervised learning (OWSSL) extends traditional semi-supervised learning to open-world scenarios by identifying novel categories in unlabeled data, thereby enhancing the model's generalization capability. However, existing OWSSL datasets typically assume a balanced class distribution, whereas real-world applications often exhibit highly imbalanced distributions. This imbalance makes it particularly challenging to learn tail classes and discover novel categories. This paper introduces the Class-Balanced Representation and Recognition Framework (CBTM-NCD), which uses the Variational Dirichlet Process (VDP) to improve tail class features and includes a generative data balancing strategy.Additionally, CBTM-NCD adopts a two-stage optimization strategy to identify novel category samples, effectively tackling three major challenges prevalent in open-world long-tailed scenarios in open-world long-tailed distributions: insufficient feature representation of tail classes, difficulty in discovering unknown categories, and class distribution imbalance.To enhance transparency and reproducibility, the code is available at https://github.com/wuzelei123/CBTM-NCD.