Paper:
Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete. Until very recently, deep graph matching methods start to resort to deep networks to achieve unprecedented matching accuracy. Along this direction, this paper makes two complementary contributions which can also be reused as plugin in existing works: i) a novel node and edge embedding strategy which stimulates the multihead strategy in attention models and allows the information in each channel to be merged independently. In contrast, only node embedding is accounted in previous works; ii) a general masking mechanism over the loss function is devised to improve the smoothness of objective learning for graph matching. Using Hungarian algorithm, it dynamically constructs a structured and sparsely connected layer, taking into account the most contributing matching pairs as hard attention. Our approach performs competitively, and can also improve state-of-the-art methods as plugin, regarding with matching accuracy on three public benchmarks.

Review:
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