2019

Cross Attention Network for Few-shot Classification

Hou, Ruibing, Chang, Hong, Ma, Bingpeng et al.

Understand

Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples.

The unseen classes and low-data problem make few-shot classification very challenging.
Many existing approaches extracted features from labeled and unlabeled samples independently, as a result, the features are not discriminative enough.
In this work, we propose a novel Cross Attention Network to address the challenging problems in few-shot classification.

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