Pixels to Graphs by Associative Embedding
Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph definition. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and demonstrate state-of-the-art performance on the challenging task of scene graph generation.
This paper proposes the use of a Hourglass net with associative embeddings to generate a graph (relating objects and their relationships) from an image. The model is presented as one of  end-to-end learning. The Hourglass net provides heatmaps for objects and relationships, feature vectors are extracted from the top locations in the heatmaps and used with FC layers for predicting object-classes, bounding boxes and relationships among objects. Associate embeddings are used to link vertexes and edges, each vertex having an unique embedding.  Experimental results show the high performance of the proposed methodology. 
I think this is a valuable work, yet I have doubts on whether it meets the NIPS standards. On the one hand,
* the performance of this methodology is quite good in the approached  subtasks. On the other hand, the novelty/originality of this work is somewhat limited:
* it is based in a standard Hourglass network, standard in the sense that nothing novel seems to be added to the network, it simply estimates heatmaps and the FC layers provide estimates
* it incorporates associative embeddings to generate the graph. Whereas this is an interesting approach, it is quite similar as the way it is used in [20] https://arxiv.org/pdf/1611.05424.pdf 
Hence, this work is another use for Hourglass-nets + associative embeddings as proposed in [20] obtaining good performance. I do not think this is enough contribution to be presented at NIPS. 
Further detailed comments:
* The authors themselves acknowledge one of their contribution is a "November el use" of associative embeddings. Whereas I agree there is novelty on the usage of this methodology, the contribution in this side is limited (in the nips sense)
* The authors fail at explaining both, the details and intuition of the proposed methodology (in all parts of the paper, but mostly in the intro, where this info is mostly important). The authors, assume the reader has the same degree of understanding of their proposed methodology ( not only of general technical stuff related to the approach, but also of methodology  itself), making it kind of tedious to read the paper, and a bit complicated to follow it
* Authors should make more explicit the differences and emphasize the importance of the contribution of this paper, certainly it is a different problem than that approached in [20], but is that difference enough contribution as to publish a paper in NIPS?, please argue on the differences and provide arguments supporting the strength of your contribution 
I removed a comment on overlap, my recommendation is not based on such assumption 
Typo 
* "them. [18]"