Attention mechanisms have been boosting the performance of deep learning models on a wide range of applications, ranging from speech understanding to program induction. However, despite experiments from psychology which suggest that attention plays an essential role in visual reasoning, the full potential of attention mechanisms has so far not been explored to solve abstract cognitive tasks on image data. In this work, we propose a hybrid network architecture, grounded on selfattention and relational reasoning. We call this new model Attention Relation Network (ARNe). ARNe combines features from the recently introduced Transformer and the Wild Relation Network (WReN). We test ARNe on the Procedurally Generated Matrices (PGMs) datasets for abstract visual reasoning. ARNe excels the WReN model on this task by 11.28 ppt. Relational concepts between objects are efficiently learned demanding only 35% of the training samples to surpass reported accuracy of the base line model. Our proposed hybrid model, represents an alternative on learning abstract relations using self-attention and demonstrates that the Transformer network is also well suited for abstract visual reasoning.
This work proposes a new architecture for abstract visual reasoning, based on Transformer-style soft attention and relation networks. The authors test their network on the PGM dataset, and demonstrate a non-trivial improvement over previously reported baselines. 
In general, abstract reasoning is an important field of current study in neural network-based machine learning, as it is an area that has notoriously eluded these types of models historically. The paper is reasonably well put together, and I have no reason to question the various technical aspects of the work.
Unfortunately, I think there are significant shortcomings. Firstly, the PGM dataset was designed to stress out-of-distribution generalization, and performance on the Neutral split was not proposed as a particularly interesting challenge on its own. This is because, as the name implies, abstract reasoning requires the ability to identify abstract conceptual features of the data and compose them in novel ways at test time, which is *not* a feature of the neutral split.  The authors are encouraged to run their model on these other generalization splits. 
Second, there seems to be little value to the field overall for research involving minor architectural improvements for single datasets. If the authors believe in this method, they are encouraged to demonstrate its effectiveness on a wide variety of data types. On this note, I should add that the authors are incorrect to state that this is the first work to use self-attention for abstract reasoning (please see Zambaldi, 2018 for one example of many papers that have incorporated self-attention into convolutional architectures). 
So to sum up, while this work broaches an interesting subject and is technically fine, it does not surpass the threshold for acceptance because it fails to demonstrate the usefulness of the method on the task at hand, as well as broad utility of the proposed method.