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: This paper proposes to improve efficient video recognition on low-resolution frames by cross-resolution knowledge distillation (ResKD). Specifically, the student network with shallower architecture and lower-resolution frames is guided by a teacher network with deeper architecture and higher-resolution inputs. By distilling the learnt knowledge from teacher network, ResKD narrows the performance gap between efficient model and large model. The experiments are conducted on several public video recognition datasets. The results validate the good performance-efficiency trade-off achieved by ResKD.
strengths_and_weaknesses: Strengths:

+ The paper is mostly clear and easy to follow.
+ The good performance on several public datasets validate the effectiveness of ResKD.

Weaknesses:

- Novelty. The basic idea of this paper is to distill the knowledge from large-scale teacher model. However, utilizing the popular knowledge distillation (KD) for video recognition is not new (e.g., [34], [35]). It is not surprising that KD from a powerful model could benefit efficient models. Especially, the KD between networks with different input frame resolutions for video recognition has been proposed in [34]. This paper is more like a technical report that validates the existing KD technique on efficient video recognition. Given these points, the technical contribution of this paper is limited.
- Insights on video data. Considering this paper mainly focuses on video recognition, some insights on video data should be given. However, the proposed ResKD ignores the temporal redundancy in videos and only studies the spatial redundancy. ResKD is more like a general low-resolution image classification framework and has no special design for video data.
- Table 1. In Table 1, the depth of ResNet (in Backbones column) should be listed. The performance of ResKD is lower than AdaFocus V2 on FCVID, however, ResKD is marked as the best result, which may mislead the readers.
- Throughput comparison. This paper only lists the throughput in Table 4. Considering that GFLOPs cannot fairly measure the efficiency of a network, the throughput comparisons between ResKD and baseline methods should be given.

questions: My main concerns are novelty and insights. Please see the details in Strengths and Weaknesses part.
limitations: The limitations and potential negative societal impact are well discussed in the paper.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper analyzed the underlying causes of performance degradation on low-resolution frames, and proposed a cross-resolution knowledge distillation (ResKD) to bridge the gap between network and input size. This work is interesting.
strengths_and_weaknesses: The problems are clearly stated, and the manuscript is well written.
Some experimental conditions are not clearly described.
questions: In Section 4.5, it is not clear how the student Backbone is trained for each different low resolution.
limitations: NA.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes resolution distillation for efficient video recognition (ResKD).
ResKD adopts the typical knowledge distillation framework, which consists of a teacher network (with high resolution) and a student network (with low resolution). 
ResKD calculates the MSE loss between teacher network feature and the upsampled student network feature for knowledge distillation.
The authors conducted extensive experiments to validate the efficacy of this framework.
strengths_and_weaknesses: ## Strengths
1. ResKD is a simple & effective idea for efficient video understanding. 
2. Extensive experiments have been conducted to validate the effectiveness of this framework. 

## Weakness
1. This paper simply adopts the typical knowledge distillation framework, which lacks novelty to some extent.
2. The section 3 of this paper seems to be trivial and not closely related to ResKD. 
   1. If the backbone is unchanged and the spatial resolution is reduced, the recognition performance may severely suffer from the over early down-sampling and the drastically reduced computation amounts (4x reduced for 224 -> 112, *etc.*).
   2. However, if you remove a down-sampling layer in the early stage, the computation amounts would be close to the original network with high resolution. Thus that's not a good idea for efficient video understanding. 

In summary, I think this paper is a good empirical study, but lacks novelty. 
questions: 1. Ablation study: In this paper, you present the results of using the last-layer feature for ResKD, have you tried features at other layers or to use features at multiple stages for ResKD?
2. What's the recognition performance for this setting: Dataset: Kinetics400, Teacher: Swin_S (224), Student: Swin_S (112). 
limitations: Yes.
review_title: null

Meta-review: metareview: After the rebuttal and discussion, two reviewers recommend acceptance, one borderline rejection. Most concerns of the raised in the borderline review were addressed at a sufficient detail in the rebuttal. The AC sees no reason the reject this paper.
confidence: Certain
Decision: Accept