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Few-shot learning presents a challenge that a classifier must quickly adaptto new classes that do not appear in the training set, given only a few labeledexamples of each new class. This paper proposes a position-aware relationnetwork (PARN) to learn a more flexible and robust metric ability for few-shotlearning. Relation networks (RNs), a kind of architectures for relationalreasoning, can acquire a deep metric ability for images by just being designedas a simple convolutional neural network (CNN) [23]. However, due to theinherent local connectivity of CNN, the CNN-based relation network (RN) can besensitive to the spatial position relationship of semantic objects in twocompared images. To address this problem, we introduce a deformable featureextractor (DFE) to extract more efficient features, and design a dualcorrelation attention mechanism (DCA) to deal with its inherent localconnectivity. Successfully, our proposed approach extents the potential of RNto be position-aware of semantic objects by introducing only a small number ofparameters. We evaluate our approach on two major benchmark datasets, i.e.,Omniglot and Mini-Imagenet, and on both of the datasets our approach achievesstate-of-the-art performance with the setting of using a shallow featureextraction network. It's worth noting that our 5-way 1-shot result on Omnigloteven outperforms the previous 5-way 5-shot results.

PARN: Position-Aware Relation Networks for Few-Shot Learning