Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: This work is expressed clearly and well written. 

The authors propose a new method to learn graph matching. It contributes in two aspects: 1) a new edge embedding strategy and 2) Hungarian attention incorporating with the loss function. A set of experiments as well as ablation studies have been conducted to show the effectiveness of the method.

However, my concerns are:
1) Is the graph matching algorithms only applied in the field of image matching？How about other fields？
2) And if not, is it more convincing to conduct more experiments across other related fields?
3) It will be better to give algorithm complexity and parameter analysis with the state-of-the-art algorithms since many additional operations are added.
review_title: Official Blind Review #4

final_score: rating: 6: Weak Accept
review_content: The authors proposed a new way to train graph siamese networks for the graph matching problem. The overall framework of this paper is somehow similar to [1] and [2], except for the final Hungarian attention module, which is the key contribution of this paper. In the current settings, the authors proved that using their Hungarian attention module, the performance can be improved. However, it would be better if the Hungarian attention can be applied to DGCNN in [1] and CMPNN in [2]. It would be good if the author can do an extra experiment to apply the Hungarian attention module to these two modules. Also, the authors may want to add some discussion about these two papers in the related works section (both papers do have published their codes). 

In the current experiment settings, both visual and geometric feature is used. Is it possible for the module to only using geometric features as [1] and [2]?


[1] Wang, Yue, and Justin M. Solomon. "Deep Closest Point: Learning Representations for Point Cloud Registration.", ICCV 2019,
[2] Zhen Zhang, and Wee Sun Lee. "Deep Graphical Feature Learning for the Feature Matching Problem.", ICCV 2019

review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: This paper studies the graph matching problem in the context of vision. Although I am familiar with the graph matching problem, I have much less experience regarding its application in vision. My understanding is that features are extracted from images and used to construct a graph. This graph is then passed through a GNN but there are steps which are unclear. In particular on page 5, 'm and H are fed to a GRU as a sequential input' but I do not see where this fit into the architecture. Similarly, I am a bit confused by equations (8) and (9) as there is a H^(t+1)_v in both equations. Does equation (9) make the function u_t explicit? Then what is \Gamma_N and similarly what is \Gamma_E in equation (11)?
As it is written, this paper seems more appropriate for a conference in vision. 
review_title: Official Blind Review #2

Meta-review: This paper proposed a new graph matching approach. The main contribution is a Hungarian attention mechanism, which dynamically generates links in computational graph. The resulting matching algorithm is tested on vision tasks.

The main concern of reviews is that the general matching algorithm is only tested on vision tasks. The authors partially addressed this problem by providing new experimental results with only geometric edge features. Other comments of Blind Review #2 are about some minor questions, which have also been answered by the authors.

Overall, this paper proposed a promising graph match approach and I tend to accept it. 

Decision: Accept (Poster)