  Novel category discovery aims at adapting models trained on known categories
to novel categories. Previous works only focus on the scenario where known and
novel categories are of the same granularity. In this paper, we investigate a
new practical scenario called Fine-grained Category Discovery under
Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained
categories with only coarse-grained labeled data, which can adapt models to
categories of different granularity from known ones and reduce significant
labeling cost. It is also a challenging task since supervised training on
coarse-grained categories tends to focus on inter-class distance (distance
between coarse-grained classes) but ignore intra-class distance (distance
between fine-grained sub-classes) which is essential for separating
fine-grained categories. Considering most current methods cannot transfer
knowledge from coarse-grained level to fine-grained level, we propose a
hierarchical weighted self-contrastive network by building a novel weighted
self-contrastive module and combining it with supervised learning in a
hierarchical manner. Extensive experiments on public datasets show both
effectiveness and efficiency of our model over compared methods. Code and data
are available at https://github.com/Lackel/Hierarchical_Weighted_SCL.
