Recently, the transductive graph-based methods have achieved great success in
the few-shot classification task. However, most existing methods ignore
exploring the class-level knowledge that can be easily learned by humans from
just a handful of samples. In this paper, we propose an Explicit Class
Knowledge Propagation Network (ECKPN), which is composed of the comparison,
squeeze and calibration modules, to address this problem. Specifically, we
first employ the comparison module to explore the pairwise sample relations to
learn rich sample representations in the instance-level graph. Then, we squeeze
the instance-level graph to generate the class-level graph, which can help
obtain the class-level visual knowledge and facilitate modeling the relations
of different classes. Next, the calibration module is adopted to characterize
the relations of the classes explicitly to obtain the more discriminative
class-level knowledge representations. Finally, we combine the class-level
knowledge with the instance-level sample representations to guide the inference
of the query samples. We conduct extensive experiments on four few-shot
classification benchmarks, and the experimental results show that the proposed
ECKPN significantly outperforms the state-of-the-art methods.

通过比较、挤压和校准模块的构建，利用显式类知识传播的方法，ECKPN 方法在四个少样本分类基准测试中显著优于现有方法。