Few-shot learning presents a challenge that a classifier must quickly adapt
to new classes that do not appear in the training set, given only a few labeled
examples of each new class. This paper proposes a position-aware relation
network (PARN) to learn a more flexible and robust metric ability for few-shot
learning. Relation networks (RNs), a kind of architectures for relational
reasoning, can acquire a deep metric ability for images by just being designed
as a simple convolutional neural network (CNN) [23]. However, due to the
inherent local connectivity of CNN, the CNN-based relation network (RN) can be
sensitive to the spatial position relationship of semantic objects in two
compared images. To address this problem, we introduce a deformable feature
extractor (DFE) to extract more efficient features, and design a dual
correlation attention mechanism (DCA) to deal with its inherent local
connectivity. Successfully, our proposed approach extents the potential of RN
to be position-aware of semantic objects by introducing only a small number of
parameters. We evaluate our approach on two major benchmark datasets, i.e.,
Omniglot and Mini-Imagenet, and on both of the datasets our approach achieves
state-of-the-art performance with the setting of using a shallow feature
extraction network. It's worth noting that our 5-way 1-shot result on Omniglot
even outperforms the previous 5-way 5-shot results.

本文提出了一种基于位置感知的关系网络（PARN）来学习更灵活和强韧的测度能力，以解决 few-shot learning 中的问题。通过引入可变形特征提取器（DFE）和设计双相关性注意力机制（DCA），成功地使我们的方法能够知道语义对象的位置，并在多个基准数据集上取得最先进的表现。