  Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing
highly similar sub-categories within fine-grained objects, such as different
soybean cultivars. Compared to traditional fine-grained visual categorization,
Ultra-FGVC encounters more hurdles due to the small inter-class and large
intra-class variation. Given these challenges, relying on human annotation for
Ultra-FGVC is impractical. To this end, our work introduces a novel task termed
Ultra-Fine-Grained Novel Class Discovery (UFG-NCD), which leverages partially
annotated data to identify new categories of unlabeled images for Ultra-FGVC.
To tackle this problem, we devise a Region-Aligned Proxy Learning (RAPL)
framework, which comprises a Channel-wise Region Alignment (CRA) module and a
Semi-Supervised Proxy Learning (SemiPL) strategy. The CRA module is designed to
extract and utilize discriminative features from local regions, facilitating
knowledge transfer from labeled to unlabeled classes. Furthermore, SemiPL
strengthens representation learning and knowledge transfer with proxy-guided
supervised learning and proxy-guided contrastive learning. Such techniques
leverage class distribution information in the embedding space, improving the
mining of subtle differences between labeled and unlabeled ultra-fine-grained
classes. Extensive experiments demonstrate that RAPL significantly outperforms
baselines across various datasets, indicating its effectiveness in handling the
challenges of UFG-NCD. Code is available at
https://github.com/SSDUT-Caiyq/UFG-NCD.
