Paper:
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering. In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification. In this task, an explanation depends on both of the input images, so standard methods do not apply. We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match. We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition. Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.

Review:
Overview/Contribution:
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The paper proposes an explanation mechanism that pairs the typical saliency map regions together with attributes for similarity matching deep neural networks. The authors tested their methods on two datasets, i.e. Polyvore Outfits (a clothing attributes dataset) and Animals with Attributes 2 (a dataset of attributes for animals).
Overall, the paper has merit to be accepted to the conference with the following strengths and weaknesses. I suggest to the authors to address the weaknesses pointed out to make the paper more stronger, especially adding few more attributes datasets such as person attributes datasets as noted below in the weakness section.
Strength:
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- The paper is written clearly and is easy to understand. I have seen the additional results and visual comparisons in the supplemental material and it was useful, albeit a bit longer.
- Explanations have the potential to make decisions made by a deep neural model transparent to end users among other benefits especially for sensitive applications such as healthcare and security. Explaining decisions made by similarity matching models has many applications including person attribute recognition and person re-identification for surveillance scenarios [1]. So, in this respect, this paper is relevant to the target audience.
- There is a bit of confusion between explanation and interpretation of decisions made by deep neural network models in the explainable AI literature and in most cases the two are used interchangeably. Hence, saliency maps are considered as explanation on their own by many. Combining saliency map based interpretations together with higher level concepts such as attributes has the potential to generate more realistic explanations of the decisions. The authors made this point at the second paragraph of the introduction. 
- Fig. 1 (b) also is a clear example of the kind of explanations generated using a template with the key attribute in question accompanied by the visual saliency map interpretation.
- Fig. 2 clearly shows the overall proposed method and the attribute ranking based on the attributes explanation prior and the match between the saliency map and attribute activation maps.
- The attribute ranking and selection method of informative attributes using combinations weighted TCAV and cosine similarity between the attribute activation map and the generated saliency map is novel.
Weakness:
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- Applications of such a combined explanatory system don’t seem to be highly motivated in the introduction. I suggest the authors discuss more of the image similarity based applications and less on the discussion and heavy citation of generalized deep neural networks.
- The forms of the two loss components are both variants of l_{1} and l_{2} standard losses and they could be subject to issues with the standard variants of the l_{1} and l_{2} losses such as lack of translation and other transformation invariances. Hence, it would have been more useful to give the reasoning for the selection of the losses employed compared to other similarity and divergence based losses that are less sensitive to such variations.
- Similar to the above point, the choice of cosine similarity to compare match b/n attribute activation maps and saliency maps seem arbitrary. The method is described well but why cosine similarity was chosen in terms of its benefits compared to other similarity metrics is not that clear.
- Evaluation on more datasets such as person/pedestrian attributes datasets would have demonstrated the generalizability of the proposed method across multiple practical domains. As such, I would suggest the authors test their method on at least one person/pedestrian attributes dataset such as PETA, Market1501, etc.
- Although Fig. 1 (b) motivated a more practical high level explanation, in the results section, the attribute explanations are reduced to just the selected attribute that matched with the saliency well. Human-like concise attribute-based high level explanation just like the example given in Fig. 1 (b) would have made the paper stronger. Even if NLP is beyond the scope of this paper, a simple template based explanation that incorporated the selected/matched attribute would have been more effective.
- The results are too concise and a few ablation results on different losses etc. could have helped. There is too many qualitative results especially in the supplementary.
1) Bekele, E., Lawson, W. E., Horne, Z., & Khemlani, S. (2018). Implementing a Robust Explanatory Bias in a Person Re-identification Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 2165-2172).