Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning.
The paper introduces a problem “few-shot few-shot learning” that aims to firstly transfer prior knowledge from one domain to the domain where the base training tasks reside, and then train a few-shot learning model on training tasks and apply it to novel test tasks. The two “few-shot” in the name refers to base training tasks and novel test tasks. In their algorithm, they use a model pre-trained on another dataset as the prior knowledge and fine-tune it on training tasks. During the test, they use the weighted average of samples’ representations per class as the prototype of each class, where the weight is large for samples with more discriminative prediction over pre-trained domain’s classes. Afterward, classification is reduced to finding the nearest neighbor among the class prototypes. Some experiments show that the pre-trained model can improve few-shot classification accuracy.
My major concerns:
1) They try to propose a new problem, but their description shows that the problem is exactly the same as what most “few-shot learning” works aim to solve: use a pre-trained model, train a meta-learner on few-shot training tasks, and apply it to novel test tasks. 
2) The algorithm does not have any important contributions comparing to existing ones: they define a prototype per class based on the pre-trained model and apply the nearest neighbor classification. The so-called “prototypical classifier” is actually the nearest neighbor classifier since no prototypical network structure is learned in the proposed method.
3) I would not call the weighted average as “attention” because it is not: the weight in attention is computed by a module with learnable parameters, while the weight in this paper is computed by the entropy of a pre-defined model’s output prediction. 
4) The “spatial attention” only makes sense when the pre-trained domain’s classes can describe the main concepts appearing in the images of novel classes. This assumption is too strong since it requires class-level (rather than lower-level) relationships.
5) The base training is not necessary in the algorithm: it is used to only fine-tuning theta and W. As the author said in the beginning of Section 4.1, they can directly solve novel tasks based on the pre-trained model.
6) The experiments show that the pre-trained model is helpful in few-shot learning, which is a known fact.
7) The writing of this paper is very poor: a lot of typos and grammar errors, inconsistency between narratives, abuse of notations, wrong equation reference, even missing punctuations. They make the paper hard to understand.
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Update:
Thanks for the authors' rebuttal! After reading their rebuttal, I still have main concerns about the novelty of the problem and the writing quality. The proposed method tends to be incremental.