OpenDUN: To Discover Unknown Number of Visual Categories

Open-Set methods have relaxed the underlying assumption made by most image recognition studies that all samples in the test and training datasets belong to the same classes by considering only a part of classes are known in training dataset. However, most of these approaches require a known or predefined number of novel classes, which is often not the case in real applications. In this study, we aim at a more difficult but practical scenario, where the number of novel classes is unknown. By merging the unlabeled samples into clusters instead of directly assigning categorical labels to them, the proposed end-to-end framework can simultaneously estimate the number of novel classes and learn the appropriate division of unlabeled samples. In addition, a cluster scattering strategy is introduced such that the erroneous merging can be alleviated. Comprehensive experiments on three benchmark datasets are conducted to demonstrate the superiority of the proposed method in both the estimation of the novel class number and the classification of unlabeled samples.