  Deep learning based computer vision fails to work when labeled images are
scarce. Recently, Meta learning algorithm has been confirmed as a promising way
to improve the ability of learning from few images for computer vision.
However, previous Meta learning approaches expose problems:
  1) they ignored the importance of attention mechanism for the Meta learner;
  2) they didn't give the Meta learner the ability of well using the past
knowledge which can help to express images into high representations, resulting
in that the Meta learner has to solve few shot learning task directly from the
original high dimensional RGB images.
  In this paper, we argue that the attention mechanism and the past knowledge
are crucial for the Meta learner, and the Meta learner should be trained on
high representations of the RGB images instead of directly on the original
ones. Based on these arguments, we propose two methods: Attention augmented
Meta Learning (AML) and Representation based and Attention augmented Meta
Learning(RAML). The method AML aims to improve the Meta learner's attention
ability by explicitly embedding an attention model into its network. The method
RAML aims to give the Meta learner the ability of leveraging the past learned
knowledge to reduce the dimension of the original input data by expressing it
into high representations, and help the Meta learner to perform well. Extensive
experiments demonstrate the effectiveness of the proposed models, with
state-of-the-art few shot learning performances on several few shot learning
benchmarks. The source code of our proposed methods will be released soon to
facilitate further studies on those aforementioned problem.
