Paper:
Convolutional neural networks (CNNs) have been generally acknowledged as one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major obstacles on the road to achieving ideal machine intelligence. One is that CNNs are complex and hard to interpret. Another is that standard CNNs require large amounts of annotated data, which is sometimes very hard to obtain, and it is desirable to be able to learn them from few examples. In this work, we address these limitations of CNNs by developing novel, simple, and interpretable models for few-shot learning. Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs. We first adapt the learning of visual concepts to the few-shot setting, and then uncover two key properties of feature encoding using visual concepts, which we call category sensitivity and spatial pattern. Motivated by these properties, we present two intuitive models for the problem of few-shot learning. Experiments show that our models achieve competitive performances, while being much more flexible and interpretable than alternative state-of-the-art few-shot learning methods. We conclude that using visual concepts helps expose the natural capability of CNNs for few-shot learning.

Review:
My main concern for this paper is that the description of the Visual Concepts is completely unclear for me. At some point I thought I did understand it, but then the next equation didnt make sense anymore... If I understand correctly, f_p is a representation of *all images* of a specific layer *k* at/around pixel "p", (According to last line of page 3). That would make sense, given that then the dimensions of the vector f_p is a scalar (activation value) per image for that image, in layer k, around pixel p. Then f_v is one of the centroids (named VCs). However, this doesnt seem to be the case, given that it is impossible to construct VC activations for specific images from this definition. So, it should be something else, but it does not become clear, what this f_p is. This is crucial in order to follow / judge the rest of the paper. Still I give it a try.
Section 4.1 is the second most important section of the paper, where properties of VCs are discussed. It has a few shortcomings. First, iIt is unclear why coverage should be >=0.8 and firerate ~ 1, according to the motivation firerate should equal to coverage: that is each pixel f_p is assigned to a single VC centroid. Second, "VCs tent to occur for a specific class", that seems rather a bold statement from a 6 class, 3 VCs experiment, where the class sensitivity is in the order 40-77%. Also the second experiment, which shows the spatial clustering for the "car wheel" VC, is unclear, how is the name "car wheel" assigned to the VC? That has have to be named after the EM process, given that EM is unsupervised. Finally the cost effectiveness training (3c), how come that the same "car wheel" (as in 3b) is discovered by the EM clustering? Is that coincidence? Or is there some form of supervision involved? 
Minor remarks
- Table 1: the reported results of the Matching Network are different from the results in the paper of Vinyals (2016).
- It is unclear what the influence of the smoothing is, and how the smoothing parameter is estimated / set.
- The VCs are introduced for few-shot classification, unclear how this is different from "previous few-shot methods" (sect 5). 
- 36x36 patches have a plausible size within a 84x84 image, this is rather large, do semantic parts really cover 20% of the image?
- How are the networks trained, with what objective, how validated, which training images? What is the influence of the layer on the performance? 
- Influence of the clustering method on VCs, eg k-means, gaussian, von-mises (the last one is proposed)?
On a personal note, I've difficulties with part of the writing. For example, the introduction is written rather "arrogant" (not completely the right word, sorry for that), with a sentence, like "we have only limited insights into why CNNs are effective" seems overkill for the main research body. The used Visual Concepts (VCs) were already introduced by other works (Wangt'15), and is not a novelty. Also the authors refer to another paper (about using VCs for detection) which is also under submission (somewhere). Finally, the introduction paragraph of Section 5 is rather bold, "resembles the learning process of human beings"? Not so sure that is true, and it is not supported by a reference (or an experiment). 
In conclusion:
This paper presents a method for creating features from a (pre-trained) ConvNet. 
It clusters features from a specific pooling layer, and then creates a binary assignment between per image extracted feature vectors and the cluster centroids. These are used in a 1-NN classifier and a (smoothed) Naive Bayes classifier. The results show promising results, yet lack exploration of the model, at least to draw conclusions like "we address the challenge of understanding the internal visual cues of CNNs". I believe this paper needs to focus on the working of the VCs for few-shot experiments, showing the influences of some of the choices (layer, network layout, smoothing, clustering, etc). Moreover, the introduction should be rewritten, and the the background section of VCs (Sect 3) should be clarified. Therefore, I rate the current manuscript as a reject. 
After rebuttal:
The writing of the paper greatly improved, still missing insights (see comments below). Therefore I've upgraded my rating, and due to better understanding now, als my confidence.