Few-shot learning aims at rapidly adapting to novel categories with only a
handful of samples at test time, which has been predominantly tackled with the
idea of meta-learning. However, meta-learning approaches essentially learn
across a variety of few-shot tasks and thus still require large-scale training
data with fine-grained supervision to derive a generalized model, thereby
involving prohibitive annotation cost. In this paper, we advance the few-shot
classification paradigm towards a more challenging scenario, i.e.,
cross-granularity few-shot classification, where the model observes only coarse
labels during training while is expected to perform fine-grained classification
during testing. This task largely relieves the annotation cost since
fine-grained labeling usually requires strong domain-specific expertise. To
bridge the cross-granularity gap, we approximate the fine-grained data
distribution by greedy clustering of each coarse-class into pseudo-fine-classes
according to the similarity of image embeddings. We then propose a
meta-embedder that jointly optimizes the visual- and semantic-discrimination,
in both instance-wise and coarse class-wise, to obtain a good feature space for
this coarse-to-fine pseudo-labeling process. Extensive experiments and ablation
studies are conducted to demonstrate the effectiveness and robustness of our
approach on three representative datasets.

本文提出了一种基于元学习的跨层级少样本分类方法，通过基于图像嵌入的贪心聚类来近似细粒度标注，同时引入元嵌入器使得模型可以在训练时仅观察粗略标签，并在测试时执行细粒度分类，实验结果表明该方法的有效性和鲁棒性。