Paper:
DISJOINT MAPPING NETWORK FOR CROSS-MODAL MATCHING OF VOICES AND FACES
We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different modalities by mapping them individually to their common covariates. These shared representations can then be used to find the correspondences between the modalities. We show empirically that DIMNet is able to achieve better performance than the current state-of-the-art methods, with the additional benefits of being conceptually simpler and less data-intensive. The code is made available at https://github.com/ydwen/DIMNet.

Review:
# Summary
The article proposes a deep learning-based approach aimed at matching face images to voice recordings belonging to the same person. 
To this end, the authors use independently parametrized neural networks to map face images and audio recordings -- represented as spectrograms -- to embeddings of fixed and equal dimensionality. Key to the proposed approach, unlike related prior work, these modules are not directly trained on some particular form of the cross-modal matching task. Instead, the resulting embeddings are fed to a modality-agnostic, multiclass logistic regression classifier that aims to predict simple covariates such as gender, nationality or identity. The whole system is trained jointly to maximise the performance of these classifiers. Given that (face image, voice recording) pairs belonging to the same person must share equal for these covariates, the neural networks embedding face images and audio recordings are thus indirectly encouraged to map face images and voice recordings belonging to the same person to similar embeddings.
The article concludes with an exhaustive set of experiments using the VGGFace and VoxCeleb datasets that demonstrates improvements over prior work on the same set of tasks.
# Originality and significance
The article follows-up on recent work [1, 2], building on their original application, experimental setup and model architecture. The key innovation of the article, compared to the aforementioned papers, lies on the idea of learning face/voice embeddings to maximise their ability to predict covariates, rather than by explicitly trying to optimise an objective related to cross-modal matching. While the fact that these covariates are strongly associated to face images and audio recordings had already been discussed in [1, 2], the idea of actually using them to drive the learning process is novel in this particular task.
While the article does not present substantial, general-purpose methodological innovations in machine learning, I believe it constitutes a solid application of existing techniques. Empirically, the proposed covariate-driven architecture is demonstrated to lead to better performance in the (VGGFace, VoxCeleb) dataset in a comprehensive set of experiments. As a result, I believe the article might be of interest to practitioners interested in solving related cross-modal matching tasks.
# Clarity
The descriptions of the approach, related work and the different experiments carried out are written clearly and precisely. Overall, the paper is rather easy to read and is presented using a logical, easy-to-follow structure.
In my opinion, perhaps the only exception to that claim lies in Section 3.4. If possible, I believe the Seen-Heard and Unseen-Unheard scenarios should be introduced in order to make the article self-contained. 
# Quality
The experimental section is rather exhaustive. Despite essentially consisting of a single dataset, it builds on [1, 2] and presents a solid study that rigorously accounts for many factors, such as potential confounding due to gender and/or nationality driving prediction performance in the test set. 
Multiple variations of the cross-modal matching task are studied. While, in absolute terms, no approach seems to have satisfactory performance yet, the experimental results seem to indicate that the proposed approach outperforms prior work.
Given that the authors claimed to have run 5 repetitions of the experiment, I believe reporting some form of uncertainty estimates around the reported performance values would strengthen the results.
However, I believe that the success of the experimental results, more precisely, of the variants trained to predict the "covariate" identity, call into question the very premise of the article. Unlike gender or nationality, I believe that identity is not a "covariate" per se. In fact, as argued in Section 3.1, the prediction task for this covariate is not well-defined, as the set of identities in the training, validation and test sets are disjoint. In my opinion, this calls into question the hypothesis that what drives the improved performance is the fact that these models are trained to predict the covariates. Rather, I wonder if the advantages are instead a "fortunate" byproduct of the more efficient usage of the data during the training process, thanks to not requiring (face image, audio recording) pairs as input.
# Typos
Section 2.4
1) "... image.mGiven ..."
2) Cosine similarity written using absolute value |f| rather than L2-norm ||f||_{2}
3) "Here we are give a probe input ..."
# References
[1] Nagrani, Arsha, Samuel Albanie, and Andrew Zisserman. "Learnable PINs: Cross-Modal Embeddings for Person Identity." arXiv preprint arXiv:1805.00833 (2018).
[2] Nagrani, Arsha, Samuel Albanie, and Andrew Zisserman. "Seeing voices and hearing faces: Cross-modal biometric matching." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.