Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: This paper proposed a local-global contextual adaptation method for the bottom-up human pose estimation.  In the proposed method, local keypoint expansion maps (KEMs) and global KEMs are both extracted. The pose-sensitive kernels (keypoint attraction maps, KAMs) are learned from the local KEMs and then performed to the global KEMs. The final prediction is generated from the refined global KEMs. The proposed method is end-to-end trainable and achieves state-of-the-art performance on COCO and OCHuman datasets.

main_review: Pros:
- This paper proposed an interesting way to refine the results of center-offset-based pose estimation. 
- The proposed method outperforms the state of the art on COCO and OCHuman datasets.
- The details of the proposed method are well described. 

Cons:
-The experiment is not sufficient to prove the effectiveness of the proposed method. More ablation studies and analyses should be provided.
1)  The KAM is the key component of the proposed method, so it is better to investigate different ways for KAM generation. For example, different sizes of local KEMs, different designs of convolutional message passing module, use or do not use KAM to refine the global KAMs. 
2) Some proposed modules should be evaluated in the ablation studies, e.g. the CMP, the attention normalization and the effectiveness of performing KAM to global KEMs, etc. 
3)  It is important to show the inference speed comparison between the baseline and the proposed LOGO-CAP. 
4) Further analysis should be provided. What kinds of KAMs kernels are generated adaptively and how do the KAMs refine the global maps? What kind of hard cases are refined by the proposed method.

-This paper claims that ‘this is the first fully end-to-end trainable method that achieves the state-of-the-art performance’ (in L. 161). However, many end-to-end trainable pose estimation methods have been proposed, e.g. Mask R-CNN, ICCV 2017,  HGG[13]. 

-The paper should be further polished before being published.
1) More explanations should be presented in the caption of each figure, e.g. Figure 1, 3, 4. 
2) In Figure 4, the major difference between the initial offsets and the refined pose should be highlighted. It is hard to find the improvements. 
3) Typos:
In L.45, ‘approach is ’ -> ‘approaches are’
In L. 280, the citation is missed.
In L. 319, 321, 328, ‘points’ should be ‘%’.  

limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 3: Clear rejection
review_content: summary: This paper introduces an end-to-end human pose estimation method, termed LOGO-CAP. It proposes a local-global contextual adaptation formulation and applies KAMs and the global KEMs to refine the estimated center-offset poses. It achieves 72.2 mAP on COCO val-2017 dataset. 
main_review: Pros:
This work proposes a novel local-global contextual adaptation formation for accurate pose estimation. Comparing with bottom-up center-offset methods, LOGO-CAP achieves a better trade-off between accuracy and efficiency, which is important in the task of human pose estimation.

Cons:
1.	This paper argues that “it is the first fully end-to-end trainable method that achieves state-of-the-art performance”, which is over-claimed. 1) There are several end-to-end methods for human pose estimation (e.g., Mask RCNN, bottom-up methods [35, 20] and bottom-up regression methods [28, 11]); 2) LOGO-CAP does not achieve the state-of-the-art performance (SimpleBaseline[32], HRNet[27]).
2.	The “Slow” keypointer, which introduces KAMs and the global KEMs to refine the poses estimated by center-offset formulation, is applied at person instance level (or even at joint level). It is more like a top-down approach rather than a bottom-up approach. It is likely that the center-offset formulation only provides the coarse position of persons or joints, then they are processed individually. Their inference time increases with the number of people. The reviewer believes that both top-down approaches and bottom-up approaches should be compared in Table2.
3.	This paper lacks of ablation study to show the effect of proposed modules. It should clearly introduce where the improvement over the baseline come from. The following experiment results should be given: a) the effect of local-global contextual adaptation; b) the effect of Gaussian reweighting; c) the effect of convolution message passing module; d) The reviewer is also curious about the result of applying the local window search (the argmax) on the KAMs, which is similar to Table.1 Emp. Bound. 
4.	The inference time of the proposed method increases with the number of persons, as mentioned above. The details of how to test the inference speed for multi-person images and the reasons why LGO-CAP show superiority over the bottom up methods (DEKR[11]) on inference speed should be given.
5.	The inaccuracy of global KEMs comes from the global keypoint heatmaps, while the KAMs measure the error of center-offset results. Here is the mismatch between heatmaps and center-offsets. How can KAMs help refine global KEMs? 
6.	The writing can be improved. Notations in this paper are not well defined. There are also too many similar concepts with different names, e.g. Local heatmaps, local KEMs, KAMs. It would be better if they use a consistent name throughout the paper. Some important details of the proposed approach are missing (e.g., how to compute the geometric mesh of the global KEMs). 
limitations_and_societal_impact: Yes. The authors adequately described the limitations of the work. 
This paper does not have obvious potential negative societal impact.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper proposes a center-offset based bottom-up human pose estimation architecture. The proposed method first estimates the centers and offsets of keypoints to get the initial poses. Then for each keypoint, estimate the parameters of an 11x11 convolution kernel in an instance-aware way. Finally, for each keypoint, a larger window is extracted from the global heatmap, and the learned 11x11 kernels are applied to convolve the extracted heatmap.

main_review: The method proposed by this paper is novel and technically sound. 
The idea of combining keypoint features that belong to the same person to obtain the convolution kernel of a single keypoint is interesting. Extracting a 97x97 pose-guided heatmap for each keypoint introduces high resolution to the bottom-up architecture, what is exactly the bottom-up architecture needed.
Of course, this paper also has some problems. First, the "Fast Keypointer" and "Slow Keypointer" mentioned at the beginning of this paper do not actually relate to speed. The "Slow Keypointer" proposed is actually a refinement of the coarse estimated pose. This statement affects readers' understanding to a certain extent. Second, there are many tricks used in this paper, such as Attention Normalization, reweigh heatmaps using gaussian kernel and OKS loss. But ablation study has not been carried out in the experiment to show the influence of these tricks. Adding an experiment like this would be more convincing. Last, since each keypoint of each person has to be dealt with separately, the speed of the method proposed in this paper should be greatly affected by the number of people in an image, and the memory usage should also be large, but this paper does not mention it. I don’t know if some techniques are used to alleviate these two problems.
All in all, the conclusions of this paper are significant. After DEKR, it proves again that the center-offset architecture can perform high-precision pose estimation.

limitations_and_societal_impact: Since each keypoint of each person has to be dealt with separately, the speed of the method proposed in this paper should be greatly affected by the number of people in an image, and the memory usage should also be large, but this paper does not mention it. If so, the author can add some relevant descriptions to the paper. If not, the author can mention it in the paper and leave it for follow-up work.
review_title: null

Meta-review: This submission received 3 strongly diverging final ratings: 4, 3, 8.
On the positive side, reviewers appreciated the central idea and strong empirical performance.
At the same time, most reviewers felt like ablations were insufficient, some of the claims were inaccurate, and overall the submission needed another major revision and a round of peer reviews before it can be considered for publication.
After an extensive discussion between the authors and the reviewers during the rebuttal period, the ratings remained unchanged.
Overall, the AC agrees with the skeptical reviewers that the weaknesses of this submission at this point outweigh its strengths, so the final recommendation is to reject.
Decision: Reject