Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper proposes a Branch-to-Trunk network (BTNet) with multiple independent branch networks (BNets) and a shared trunk network (TNet) to extract feature representation for multi-resolution face recognition. The CurricularFace loss is refined as influence loss and a branch distillation loss is included for training to ensure the discriminative and compatible representation. The experiments conducted on both 1:1 and 1:N verification seems to validate the efficacy of the proposed method.
strengths_and_weaknesses: ### Strengths:

- The paper is well organized and easy to follow.

- It is an interesting idea to decouple a face image as branches for discriminative representation learning and then couple them as the trunk for compatible representation learning. 

- The performance on the tasks of both cross-resolution and same-resolution identify matching looks very promising. 


### Weaknesses:

- The authors claim the proposed BTNet is able to learn unified representation for face recognition. However, the experiment validation is only conducted on the face verification task. Will the learned unified representation can be beneficial to solve the general face recognition tasks?

- The comparisons is inadequate to well demonstrate the superiority of the proposed BTNet.  All the baselines listed in Table 2 are at least two years ago.  The authors should compare the proposed BTNet with some recent face identification methods. 

- The necessary ablation studies are missing. In the current experimental results presentation, we only can see the overall performance gain. It is hard to guess which parts contribute more, and which parts contribute less. If the main performance gain is due to the introduction of CurricularFace loss as influence loss, then main technical contribution of TBNet may need to be doubted.

- Lack the necessary qualitative results to show both successful and failure cases.
questions: See the above "weakness".
limitations: Yes.
review_title: null

final_score: rating: 3: Reject: For instance, a paper with technical flaws, weak evaluation, inadequate reproducibility and incompletely addressed ethical considerations.
review_content: summary: In this paper, the authors introduce a Branch-to-Trunk Network (BTNet) for multi-resolution Face Recognition. 
Particularly, BTNet consists of two main components: BNet and TNet. While TNet aims at maintaining the main features flow (from high- to low-resolution), BNet is introduced for maps inputs to intermediate features with same resolution and maintain the feature compatibility when injecting them to the main flow of TNet.
The proposed BTNet is validated on QMUL-SurvFace and IJB-C protocols.
strengths_and_weaknesses: STRENGTH
- The paper is well-motivated.
- The writing is easy to follow.
- The idea of multi-branch network to overcome the limitation of the amount of information can be extracted from low-resolution input faces is interesting.
- Experimental results show improvements in comparison to baselines and prior works.


WEAKNESS
Generally, the novelty of the paper is limited as most of the building blocks (i.e. loss from CurricularFace [3] and common distillation loss) are introduced in previous works.
Although the idea of a multi-branch network to overcome the robustness against low-resolution images, there are several concerns about this design. Please see the Question Section for further comments and questions.
questions: 1. The improvement achieved by BTNet is unclear: 
BTNet aims at training TNet for the main embedding flow, BNet aims at adopting the feature map extracted from the direct low-res images for making its features compatible to the feature flow of TNet. Moreover, only L_cur is adopted for the feature discriminative as [3]. 
How can BTNet achieve improvements in comparison to prior works? How can it leave a large gap in comparison to phi_{mr} when same L_cur is adopted?

2. Some baselines are missing:
- In most experiments, phi_{hr}, phi_{mm}, and phi_{mr} are employed. However, in my opinion, these settings are quite “naive”, i.e. phi_{mr} is simply augmentation of multi-resolution images with similar weights for both HR and LR images. 
They are acceptable for some basic baselines. The authors are recommended to have more baselines such as lower weight for LR images during training or enforce the features of LR images closer to the HR ones. 
Then, the baselines would be more complete.

3. In Table 3, why is the accuracy of phi_{mm} higher than phi_{mr} in cross-resolution experiments? How about the compatibility of phi_{mm} in cross-resolution recognition? 

4. The comparisons in Table 3, 4 are lacking of prior works on IBJ-C.
limitations: Yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes a representation learning method, called Branch-to-Trunk network (BTNet), for multi-resolution face recognition. It consists of a trunk network (TNet) given in the form of a unified encoder, and multiple branch networks (BNets) that are used for resolution adaptation. A resolution-specific BNet is used on the input and the output are implanted as feature maps in the feature pyramid of TNet, at a layer with the same resolution. Using branch distillation and backward-compatible training, BTNet transfers high-resolution information to multiple branches, while keeping representation compatibility. Experiments show effective performance on face recognition benchmarks, both for multi-resolution identity matching and feature aggregation.
strengths_and_weaknesses: Strengths
- Code has been included in the submission supplementary files.
- Experiments have been provided on several datasets. 

Weaknesses
The presentation of the results should be improved, Authors should better comment the different cases and propose explanation for specific situations where results are not intuitive in the tables.

The selection strategy for the optimal branch is not fully investigated. Author acknowledged this while exposing the limitations of their work, but I think this is something very relevant to the method that required more in depth understanding.

Performance outperform state-of-the-art on one dataset, while this does not occur on others. Authors should report motivations on why this happens. Which are the dataset characteristics that best explain this fact?

The paper writing needs some improvements. There is also some typos to check. See for example:
- page 3, line 111: "(3) This also effectively reduce" --> reduces

============= POST REBUTTAL ================
The rebuttal partially answered to my questions. I prefer to change my score to borderline accept. 

questions: Did the authors perform experiments on the branch selection process? Even preliminary tests would be useful.

A figure reporting on overview of the proposed network it would have been useful.

See also weaknesses above. 
limitations: Limitations have been discussed by the authors.

No ethical issues have been reported.
review_title: null

Meta-review: metareview: This paper proposes a Branch-to-Trunk network with multiple independent branch networks and a shared trunk network for multi-resolution face recognition. This paper received three detailed reviews.  While there are some merits in this work, the reviewers raised many concerns, including  1) inadequate experiments to demonstrate the superiority of the proposed method, 2) missing ablations, 3) improvement achieved by BTNet is unclear.  After reading the reviews, rebuttals, and the paper, the AC concurs with the reviewers’ comments, and feels that the concerns outweigh the strength. Therefore, a rejection is recommended.
confidence: Certain
Decision: Reject