Dear Reviewer z46M,

Thank you for your inspiring affirmation, detailed review and insightful queries regarding our paper. Your feedback is invaluable in improving our work. Below, we address each of your points to clarify our methodology and findings.

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W1: What if a new augmentation is introduced?

R1: We acknowledge that for a newly added basic augmentation, some evaluation is still required to select possible advanced augmentation combinations. However, as described in the previous responses to W3 raised by Reviewer 4hx9 and W1 raised by Reviewer r3Sz, thanks to the proposed paradigm for generating superior augmentations in this paper, we can simplify and speed up this process, requiring only a small amount of workload.

Specifically, for the newly added basic augmentations, we first need to evaluate and rank their difficulty to get a preliminary understanding of their effects and usability. Then, we can roughly judge which augmentations are mutually exclusive or synergistic based on experience. For example, occlusion of keypoints (e.g., $T_{JC}$ and $T_{JO}$) and randomly generated occlusion (e.g., $T_{CO}$ and $T_{CM}$) are essentially synergistic. We can also roughly classify and filter through singular value decomposition (SVD) analysis, such as that shown in Figure 11. After that, we will confidently recommend new and stronger augmentation combinations based on the three concise criteria proposed in Section 3.1, including not using MixUp, not repeating with the same type of augmentation, and not stacking too many augmentations.

Finally, let's give an additional example. For instance, in Section 5.1, we introduced a new basic augmentation, YOCO (You Only Cut Once), which is based on other existing augmentations such as RandAugment or TrivialAugment. We have actually evaluated its basic effect in Table 6, which is roughly the same difficulty as $T_{JC}$ or $T_{JO}$. Then, considering that YOCO's operation is to crop and re-stitch the same image, it is a completely new type in form. Therefore, it is easy to think that it can be combined with the strongest augmentation $T_{JOCO}$ or $T_{JCCM}$ that we have already introduced, that is, to get a better combination $T_{JOCO+YOCO}$ or $T_{JCCM+YOCO}$. The process is not very time-consuming or difficult to understand, so it has the potential and value to be widely promoted for unsupervised data augmentation generation.

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W2: Training time after using augmentations combination

R2: Thanks for pointing out this important detail. In fact, the combination of different augmentations will eventually result in one augmentation after continuous execution, and will not significantly increase the overall time. The total time consumption is related to the basic augmentation itself. For example, the time consumption of $T_{JCCM}$ or $T_{JOCO}$ is similar to that of $T_{JC}$ or $T_{JO}$.

The design that actually increases the training time is another strategy we proposed, multi-path consistency loss. We have quantitatively calculated and compared the time cost of different number of path augmentations in our reply to Q2 raised by Reviewer r3Sz. Please refer to it for more details. In short, the final conclusion is that the design of multi-path loss will not significantly prolong the training time, but will bring great performance improvement. It is a simple and efficient strategy for addressing the SSHPE problem.

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W3: Stability of optimal augmentations combination

R3: This is also an important issue. In our response to W5 raised by Reviewer r3Sz, we newly added a detailed comparative experiment on the MPII dataset following the setting S1, and the trend of the final results is consistent with the conclusions on the COCO dataset in Table 3. Our method still achieved clear advantages whether using a single network or dual networks. We have updated these results in our revised paper in Appendix A.2. These further prove that the optimal augmentation combination and multi-path consistency loss we proposed are better and can continue to achieve leading results across different datasets.

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Continue in the next comment.

W4: Selection process of augmentations combination

R4: In fact, in the field of semi-supervised learning, it is not easy to discover or invent new advanced augmentations. We can find that there are still a lot of research exploring this issue to date, please refer to the third paragraph of Section 2 and the second paragraph of Section 5.3 of the main text. Under this consensus, this paper attempts to propose a standardized paradigm to produce strong augmentations, rather than just proposing a new one and ending it. Our approach is to continue to use the new basic augmentations proposed by the community to further obtain a better one. Therefore, although the advanced augmentation generation process described in Chapter 3.1 seems complicated, it provides a feasible route and can help save trial and error costs in practice. If we can eventually find superior augmentation at this cost, we think it is worthwhile and meaningful.

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W5: Quantitative analysis of augmentation difficulty ranking

R5: This is indeed an issue worth exploring in depth. As pointed out in our paper, we initially found that the approach in PoseDual was to augment the images directly on the test set. Then, in order to reflect the difficulty of different augmentations, they evaluate the same model when facing different types of augmented inputs, and record the severity of the drop in accuracy indicators. The obvious drawback of this strategy is that if we directly input a noisy image, the final accuracy will be close to 0, but such augmentation is not desirable. Therefore, our approach of utilizing ablation experiments on the training set is more intuitive and convincing. Although it seems rather heuristic, the quantitative data comparison shown in Tables 1 and 2 provides us with indisputable assurance and confidence in proposing a new superior augmentation. We also confirmed in the experimental part that this paradigm is indeed effective.

In addition, we also try to give a theoretical explanation for why different augmentations have different levels of difficulty in Appendix A.1. From the perspective of singular value decomposition (SVD), we find that the so-called advanced augmentation is more difficult because the average entropy of its singular values is higher, which means that after applying the corresponding augmentation, the augmented input may span a larger feature space, thereby helping to improve the generalization of the model. We hope that this explanation can help make up for the theoretical foundation of the proposed strategy in Section 3.1.

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W6: Ablation studies of the multi-path loss

R6: This is indeed an important concern. In fact, we have described in detail the effectiveness verification of the multi-path consistency loss strategy in the last two paragraphs of Section 3.2. For more detailed results, please refer to Table 2 and Figure 3. Among them, schemes $m_9$ and $m_{10}$ show the experiments of using the same strong augmentation to calculate two-path losses. After comprehensive comparison with scheme $m_{11}$, it can be found that different multi-path strong augmentations also have synergistic effects, which will perform better than using only a single type augmentation as in schemes $m9$ and $m_{10}$. In addition, by comparing some experiments in Table 1 and Table 2, such as $c_{10}$-$m_6$, $c_{11}$-$m_7$, $c_{12}$-$m_4$ and $c_{13}$-$m_5$, we can also find that multi-path loss itself has a positive effect, which can help alleviate the negative impact of excessive accumulation of multiple basic strong augmentations.

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W7: Reasons for selecting two optimal augmentations

R7: The reasons for choosing $T_{JOCO}$ and $T_{JCCM}$ can be found in the second and third paragraphs of Section 3.1. Specifically, first, we focus on the HPE task in this paper, and it is not beneficial to choose MixUp-related augmentations. For specific explanations, please refer to our response to W1 raised by Reviewer 4hx9. Secondly, considering the synergistic effects between different augmentations, we tend not to apply the same type of augmentation to the image. According to this criterion, we can exclude combinations $T_{COCM}$, $T_{JCCO}$, $T_{JOCM}$ and $T_{JOJC}$, leaving only $T_{JOCO}$ and $T_{JCCM}$. Finally, an intuitive idea is that we'd better not apply too many augmentations to an image, otherwise it may backfire and destroy the semantic information in the image. In extreme cases, it may directly make the image unrecognizable or meaningless. Therefore, we do not recommend stacking too many augmentations. These empirical conclusions or criteria are finally quantitatively verified in Table 1 and Figure 2.

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We hope these responses address your concerns and provide a clearer understanding of our approach and its capabilities. We are committed to making the necessary revisions to reflect these clarifications and enhance the overall quality of our paper.

Dear Reviewer iqhT,

Thank you for your detailed review, positive appreciation and critical points about our paper. Your feedback is essential for refining our work, and we have addressed each of your concerns below.

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W1: Concerns about novelty

R1: Firstly, the research goal of this paper is to try to propose a new paradigm based on existing basic augmentations, so as to obtain a new advanced augmentation combination conveniently and efficiently. Therefore, we adopt the Joint Cutout augmentation in the existing similar work PoseDual and the Joint Cut-Occlude augmentation in SSPCM as elements of the basic augmentation set. These do not conflict with the innovation of this paper.

Secondly, our proposed superior augmentation generation paradigm and multi-path consistency loss strategy do not overlap with the methods SSPCM or POST. Specifically, SSPCM is a triple-network framework that improves PoseDual by adding an auxiliary network to provide additional help for examining the quality of pseudo-labels. Our approach is different from SSPCM in both structure and core strategy. And our quantitative results in Tables 3 and 4 are clearly better than those of SSPCM. While, POST applies the most commonly used mean-teacher architecture in unsupervised domain adaptation to gradually update the teacher model to better predict pseudo labels. When training, only the student model uses backward propagation to update parameters, and the teacher model uses exponential moving average (EMA) to update parameters without gradients. It can be seen that POST is completely different from the teacher-student alternating network used in our paper.

Finally, our proposed strong augmentation generation paradigm is experimentally demonstrated to be concise and effective for the task of 2D HPE. While, PoseAug is for solving the 3D HPE problem, which usually desires a 2D-to-3D pose estimator/lifter, whose input is a series of detected 2D keypoints. It is a completely different field from our 2D keypoint detection using the RGB image as input. This means that augmentations used by these two fields are not mutually compatible.

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W2: Lack of experiments

R2: We appreciate the reviewer pointing out many other topics and researches that are somehow related to our study, such as 3D human pose estimation [3], 3D human pose and shape estimation [5], source-free domain adaptive human pose estimation [1], and human vision foundation model [4]. Although these methods show various advantages and generalization in human-related topics, they are not completely consistent with the research content SSHPE in this paper. Almost all of the above methods have not been or cannot be trained and tested on the 2D HPE datasets COCO and MPII used in this paper. Therefore, we cannot fairly or quickly compare our method quantitatively with these cross-proposition methods. Nevertheless, we agree that in future research, we would try to obtain the 2D human keypoint prediction results of the large foundation model or 3D human model in a zero-shot manner in order to compare with our method. This is indeed a more interesting and promising research area.

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W3: Provide more results

R3: Regarding the loss function effectiveness, we agree that the weight of the supervised loss and the unsupervised loss is a very important hyper-parameter. In the experiments of this paper, in order to compare fairly with previous methods (including PoseDual and SSPCM), we follow the same setting as them and use the same weight for these two losses. We did not deliberately optimize this hyper-parameter, which would be unnecessary or unfair.

Regarding the unsupervised domain adaptation HPE methods, we agree that it has something in common with the topic SSHPE in this paper, but these two research fields cannot be confused. The UDA HPE method is only applicable when there is a clear domain difference between the labeled source domain and the unlabeled target domain. However, our semi-supervised HPE research does not emphasize the domain difference between the labeled set and the unlabeled set. Therefore, it is unfair and inappropriate to directly compare the SOTA algorithm UDAPE [6] with SSHPE approaches including PoseDual, SSPCM and our method in Table 4. Nevertheless, we expect that our future research will apply the core innovations proposed in this paper (including the new superior augmentation and multi-path consistency loss) to address the UDA HEP problem.

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W4: Improve content clarity

R4: Thank you for pointing out these omissions. We have clarified these details in our updated paper. Please refer to the first paragraph of Section 3, where the revised part is marked in red color.

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We hope these revisions address your concerns and provide a more comprehensive understanding of our method and its unique contributions. We are dedicated to making the necessary updates to enhance the clarity and impact of our paper.

Dear Reviewer r3Sz,

Thank you for your high appreciation, constructive feedback and insightful questions regarding our paper. We appreciate the opportunity to clarify the aspects you have highlighted. Please find our responses to your queries below.

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W1: Selection of augmentations combination

R1: First of all, thank you for your affirmation of the effectiveness of the method we proposed to obtain new advertisements. In fact, although it seems intuitive, there is no exactly the same work before us to try to propose and verify the combination synergy effect between multiple basic augmentations. A technical route that has a different starting point from our work but very similar final results is the AutoAug families (see the second paragraph in Section 5.3), which require offline search for optimal parameters of different augmentations and are usually highly dependent on the dataset utilized.

Differently, we first ranked the difficulty of the existing basic augmentations, and then proposed three concise guidelines to recommend more advanced augmentation combinations, and used a large number of ablation experiments to verify these criteria. According to these criteria explained in Section 3.1, we do not have to verify all possible combinations one by one, and can rapidly be compatible with new basic augmentations, which provides a feasible pipeline for us to quickly obtain the optimal augmentation combination. For more details, we recommend reading our response to W3 raised by Reviewer 4hx9, which explains similar concerns.

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W2: Effects of different backbones

R2: Generally, the performance improvement in Table 4 is negatively correlated with the total number of parameters or basic capabilities of the backbone used. That is to say, for the same training and testing settings, using ResNet-50, ResNet-101 and HRNet-w48 as backbones respectively, the final performance will inevitably get better and better, regardless of whether full supervision or semi-supervision is used. Meanwhile, the predictable accuracy of the test set itself has an upper limit. This means that the higher the accuracy (here is mAP), the closer to performance saturation. Smaller absolute improvements are most likely related to the indicator limits of the evaluated dataset. Therefore, after using the most powerful HRNet-w48 as the backbone, the absolute improvement in mAP appears to be minor. As a more sensitive measure, we can calculate the relative performance improvement from SSPCM to Ours in each case.

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Method		| Backbone	| Nets	| AP	| Abs.Imp.	| Rel.Imp. 
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Supervised	| ResNet50	| 1	| 70.9	| 0.0		| ---
SSPCM		| ResNet50	| 3	| 74.2	| 3.3		| ---
Ours (Dual)	| ResNet50	| 2	| 74.6	| 3.7		| 12.12%
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Supervised	| ResNet101	| 1	| 72.5	| 0.0		| ---
SSPCM		| ResNet101	| 3	| 75.5	| 3.0		| ---
Ours (Dual)	| ResNet101	| 2	| 76.4	| 3.9		| 30.00%
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Supervised	| HRNetw48	| 1	| 77.2	| 0.0		| ---
SSPCM		| HRNetw48	| 3	| 79.4	| 2.2		| ---
Ours (Dual)	| HRNetw48	| 2	| 79.5	| 2.3		| 4.55%
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It can be found that although the absolute improvement based on HRNet is not obvious, our relative improvement cannot be ignored (about 5%). This shows that our method is not necessarily related to the network structure. In addition, we choose to use the current backbones for fair comparison with previous SOTA methods. Using more advanced backbones (such as vision transformers) to pursue higher accuracy/mAP is beyond the scope of this paper. However, our proposed method does not rule out the possibility of being applicable to other network structures, which will be left for future research.

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Continue in the next comment.

W6: Explanation of results in Table 8

R6: Similar to our explanation in W2 and W4, the small absolute performance improvement on the MPII test-set is completely understandable when using the powerful backbone HRNet-w32. In fact, the MPII dataset was released earlier than the COCO dataset, so it faces a more severe problem of accuracy saturation, or cannot keenly reflect the differences between SOTA algorithms. As an alternative, we suggest that reviewers can refer to the Table 11 in Appendix A.2, which shows obvious advantages of our method over the other two SOTA algorithms (PoseDual and SSPCM) when the label rate is lower.

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Q1: Division criteria in Table 5

A1: In fact, the upper, middle and lower areas in Table 5 show CNN-based fully supervised HPE methods, transformers-based fully supervised HPE methods and semi-supervised HPE methods respectively. We apologize for not stating this clearly, and we have included the corresponding statement in the revised paper (marked in red color in the description of setup S3).

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Q2: Training efficiency of our method

A2: This is a good and important question. In order to fairly and reasonably reflect the efficiency of our method, we follow the setting S1 (using ResNet-18 as the backbone, batch size is set to 32, total training epochs are 30, and the amount of labeled data is 1K), and conduct each experiment on four 3090 graphics cards (with each containing 24 GB memory) to compare the training time of our method with that of PoseCons and PoseDual. The strong augmentation used by PoseCons or PoseDual is $T_{JC}$. Considering that our method often uses different strong augmentations, their computation is not the main bottleneck. Therefore, in order to be fair, all strong augmentations in our method are also replaced into $T_{JC}$. Assuming that the total training time of PoseCons is one unit time T0, which is actually about 7 hours. Then the total training time of running other methods is summarized as follows.

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Method	| PoseCons	| Ours(Single,2#)	| Ours(Single,3#)	| Ours(Single,4#)	| 
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Time	|  T0		| 1.36 * T0		| 1.50 * T0		| 1.83 * T0		|
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Method	| PoseDual	| Ours(Dual,2#)		| Ours(Dual,3#)		| Ours(Dual,4#)		|
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Time	| 2.49 * T0	| 2.62 * T0		| 2.88 * T0		| 3.14 * T0		|
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where an integer with the marker # in our method means how many multi-path losses are used. From these results, we can see that when using four-path losses, although the training time increases, it is still faster than PoseDual (1.83*T0 vs. 2.49*T0). Referring to the quantitative results in Table 3 of the main paper, our method based on single-network using four-path losses achieves higher mAP than PoseDual. In addition, when using dual networks with four-path losses, the total training time does not increase significantly (2.49*T0 vs. 3.14*T0). These indicate that our method is both efficient and effective. We have added these analyses in Appendix A.5.

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Q3: The significance of Figure 7

A3: Although Table 3 has shown the quantitative comparison results, we still cannot intuitively and quickly perceive the performance differences between different methods under different annotation rates from these plain numbers. Therefore, we considerately visualize these mAP values in Figure 7. From it, we can easily see that our method not only obtains leading results, but also achieves a more obvious leading advantage when the data annotations are scarcer. This property is the most important and valuable characteristic of semi-supervised algorithms.

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Your feedback has been invaluable in helping us improve the clarity and comprehensiveness of our research. We are committed to addressing these points and enhancing the overall quality of our paper.

W3: Comparison with SSPCM

R3: As described in the setting S3 and shown in Table 5, except for SSPCM, our method still has an advantage over other fully supervised methods and the semi-supervised method PoseDual. We think there are two possible reasons: fewer training epochs and less network parameters. In fact, after submitting the paper, we trained another 400 epochs according to this setting (based on the backbone HRNet-w48, setting the batch size to 16 and using 8 A100s with 80GB of each), which took about 20 days. The final mAP and mAR values were 77.4% and 82.3%, respectively, which still did not exceed SSPCM (77.5% and 82.3%). We could not find any other better explanation, and can only conjecture that SSPCM uses a triple-network framework to bring better generalization on the COCO test-dev that has never been seen during training.

This phenomenon is indeed very weird and incomprehensible, considering that our results in other settings are significantly better than SSPCM (please refer Tables 3, 4, 8 and 9). To this end, we tried to contact the authors of SSPCM to seek their final trained model weights based on HRNet-w48 for self-testing and comparison, but we never received a response so far. And their public training code does not contain the details of this part.

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W4: Performance on the test-set

R4: Similar to our explanation in W2, it is reasonable to achieve a small absolute performance improvement on COCO test-dev where the accuracy indicator is close to saturation when using the most powerful backbone HRNet-w48. In fact, the semi-supervised HPE methods shown in Table 5 performed almost the same, which makes it difficult to significantly and objectively distinguish the advantages and disadvantages of each method. Nevertheless, we present it here for complete comparison with previous methods. Considering that the main purpose of this paper is to use semi-supervised algorithms to try to improve network performance when labels are scarce, we believe that the setting based on smaller-scale labeled data can more sensitively reflect the advantages of one SSHPE method. Please refer results in Tables 3 and 9, which contain experiments under very small annotation rates.

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W5: Instructions on dataset setup in S1

R5: Firstly, for the dataset setup in setting S1, we followed the previous work PoseDual in order to maintain fairness and reproducibility. Specifically, we select data from the first 1K, 5K, and 10K samples of the training dataset as labeled set, and the remaining samples are used as the unlabeled set. The randomness here just follows the data representation in PoseDual. To avoid ambiguity, we have corrected the statement in our main paper (marked in red color in Section 5.1). We are sorry that this oversight caused confusion to the reviewers.

As additional supplementary information, when implementing the code, we can set the number of labeled samples as TRAIN_LEN in the configuration file, and directly select the first TRAIN_LEN samples when initially loading the training dataset. You can refer to the original PoseDual code to confirm this in https://github.com/xierc/Semi_Human_Pose/blob/master/lib/dataset/coco.py#L138

Secondly, we totally agree that the conclusions drawn from testing and evaluation on small-scale data may not necessarily be generalized to other datasets. Therefore, we repeated the comparison in setting S1 and Table 3 by replacing the COCO dataset into MPII dataset. Specifically, we conducted experiments using the first 1K samples as labeled data and the left 39K samples as unlabeled data in MPII. The validation set of MPII is used to evaluate. The backbone is ResNet-18. The final comparison results are shown below.

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Methods 	| Hea 	| Sho	| Elb	| Wri	| Hip	| Kne	| Ank	| Total
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Supervised	| 89.6	| 84.8	| 72.0	| 58.4	| 57.8	| 49.4	| 41.2	| 65.3 
PoseCons	| 92.7	| 87.6	| 74.5	| 67.9	| 72.3	| 64.2	| 59.4	| 75.2
PoseDual	| 93.3	| 88.4	| 75.0	| 67.3	| 72.6	| 65.3	| 59.7	| 75.6
SSPCM		| 93.5	| 90.6	| 80.2	| 71.3	| 75.9	| 68.9	| 62.3	| 78.3
Ours (Single)	| 94.1	| 91.1	| 80.5	| 72.2	| 76.3	| 69.2	| 62.8	| 79.1
Ours (Dual)	| 94.7	| 92.4	| 81.2	| 73.3	| 76.8	| 70.6	| 63.9	| 79.7
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Not surprisingly, our method still maintains a clear lead in performance, both in terms of overall accuracy and the specific accuracy of each joint. These experiments once again demonstrate that our method is indeed universally effective and superior across different datasets. We have updated these results in our revised paper in Appendix A.2.

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Continue in the next comment.

W8: Revision of minor comments

R8: Thank you for pointing out these detailed typos. We have corrected them in the main text. At the same time, we have also added commas or periods at the end of other formulas to make the paper more standardized.

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Your feedback has been instrumental in enhancing the clarity and accuracy of our work. We are committed to making the necessary revisions to address these points thoroughly.

W4: Parameters of each augmentation

R4: Thanks a lot for pointing out this important detail. The hyper-parameters involved in each augmentation are indeed important. In order to make a fair comparison, each basic augmentation we selected is derived from various compared methods without additional fine-tuning. For example, the parameters of Joint Cutout are the same as those in PoseDual which used JC5, and the parameters of Joint Cut-Occlude are the same as those in SSPCM which used JO2. We list these parameters in Appendix A.4 so that readers can quickly and clearly know these details.

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W5: Clearer ablation studies

R5: Thank you for raising this concern. In fact, when we were conceiving the overall framework of this paper, we also struggled with the issue of how to present the ablation experiments. After much deliberation, we finally decided to present the ablation results of our proposed augmentation combination and multi-path loss in advance in Sections 3.1 and 3.2 as quantitative data for empirical studies, so that readers can quickly understand and get into the two major themes, data augmentation and consistency training. Therefore, the results in Tables 1 and 2 are essentially ablation studies. To some extent, the results in Table 3 also have the same effect, showing the comparison results of different network structures and label annotation rates. In Section 5.3, we present and compare additional important components, including other similar augmentation combinations and different training techniques. We hope that this arrangement can satisfy most readers and help them capture the key points.

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W6: Showing qualitative results

R6: Thank you for your sincere suggestion. We have added qualitative visualization comparison results in Appendix A.3, mainly including the conventional human images from the COCO val-set and the fisheye camera images from the WEPDTOF-Pose dataset. We expect these results will make our advantages more intuitively demonstrated.

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W7: The multi-path consistency loss

R7: This is indeed a good question. In fact, when designing the ablation experiments with PoseCons using a single-path loss, we chose a fixed batch size 32 to perform all experiments (all using backbone ResNet-18). All relevant results can be found in Table 1. When using multi-path consistency losses, we still set the batch size to 32, including the two-path losses (including $m_{1} \sim m_{7}$ and $m_{9} \sim m_{11}$) and four-path losses (including $m_{8}$ and $m_{12}$) in Table 2. Therefore, in final comparative experiments (see Table 3), we still keep the batch size as 32 and use the optimal four-path losses. Now, in order to investigate the possible impact of different batch sizes, we report the effects of PoseCons and PoseDual when the batch size is 128.

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Method		| Nets.	| Losses | BS   | 1K   | 5K   | 10K 
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PoseCons	| 1	| 1	 | 32   | 42.1 | 52.3 | 57.3 
PoseCons	| 1	| 1	 | 128  | 42.3 | 52.6 | 57.5 
PoseDual	| 2	| 1	 | 32   | 44.6 | 55.6 | 59.6 
PoseDual	| 2	| 1	 | 128  | 44.9 | 58.7 | 59.6 
Ours (Single)	| 1	| 4	 | 32   | 45.5 | 56.2 | 59.9
Ours (Dual)	| 2	| 4	 | 32   | 49.7 | 58.8 | 61.8
-----------------------------------------------------------

As can be seen, after increasing the batch size of PoseCons or PoseDual accordingly, the final mAP results under different labeling rates (e.g., 1K, 5K and 10K) did not get significantly better. This indicates that batch size does not have a large impact on the performance of existing methods. We also conducted additional experiments to address another concern, namely whether to use a single constant easy augmentation as input for multi-path losses (the pair {$I_e$} + {$I_{h_1},...,I_{h_n}$}, termed as 1-vs-n) or to use different easy augmentations multiple times as input (the pair {$I_{e_1}$,...,$I_{e_n}$} + {$I_{h_1}$,...,$I_{h_n}$}, termed as n-vs-n).

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Method		| Nets.	| Losses | BS   | Input	 | 1K   | 5K   | 10K 
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Ours (Single)	| 1	| 4	 | 32   | 1-vs-n | 45.5 | 56.2 | 59.9
Ours (Single)	| 1	| 4	 | 32   | n-vs-n | 45.6 | 56.4 | 59.8
Ours (Dual)	| 2	| 4	 | 32   | 1-vs-n | 49.7 | 58.8 | 61.8
Ours (Dual)	| 2	| 4	 | 32   | n-vs-n | 49.7 | 58.9 | 61.9
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As shown in the table above, whether using 1-v-n augmented input or n-vs-n augmented input, the final mAP results obtained under various labeling rates are not significantly different. This is mainly because the used easy augmentation is always fixed (e.g., $T_{A30}$), so the input does not change in essence. We have added these additional ablation studies in Appendix A.5.

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Continue in the next comment.

Dear Reviewer 4hx9,

We greatly appreciate your thorough review, constructive feedback and praise for groundbreaking on our paper. Your points have helped us identify areas for clarification and improvement. Please find our responses to your queries below.

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W1: Why not using MixUp?

R1: First, the MixUp operation is to superimpose and mix two images of the same size pixel by pixel, and the global mixing ratio $\alpha$ is randomly generated in advance. We summarize this process as ${Img} = \alpha*{Img}_1 + (1-\alpha)*{Img}_2$. Global here means the entire image is superimposed, using a predetermined blending ratio value.

In addition, MixUp is indeed used in the heatmap-based method like [R1] and performs well. We think that the main emphasis is on using MixUp to alleviate the problem of domain discrepancies in the domain transfer process of sim2real. And making it as difficult as possible for the model to distinguish between the source domain and the target domain is a common practice in the unsupervised domain adaptation from synthetic to real field. Therefore, it is reasonable that MixUp may be helpful in [R1] after mixing synthetic images and real images as input. However, our paper does not emphasize the domain adaptation setting. In each independent experiment, all labeled and unlabeled images used are from the real world and there is no obvious inter-domain differences. At this point, we lack a theoretical basis for using the MixUp operation, and our related experiments in Figure 2 and Table 1 also verify that MixUp is inappropriate for solving the SSHPE problem.

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W2: Modification of Table 1

R2: Thank you for your suggestion. We have added the data of these three base augmentations in Table 1 to facilitate quick comparison. At the same time, the corresponding convergence curves in Figure 4 have also been updated.

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W3: Principles for combining augmentations

R3: First, we need to rank the difficulty of some well-known basic augmentations, which we have already shown in Figure 2 and the first paragraph of Section 3.1. We also provide a theoretical analysis of the superior augmentation in Appendix A.1 as an explanation support. Next, we need to recognize the synergy between basic augmentations, that is, which and how many base augmentations to combine have general rules to follow. We provide an intuitive explanation in the second paragraph of Section 3.1, and its quantitative verification is shown in Table 1 and Figure 4. In practice, when combining different basic augmentations, we summarized three simple operating principles by integrating the above two pieces of information. These are introduced in the third paragraph of Section 3.1, and there are sufficient empirical experiments in the fourth paragraph to support the feasibility of these principles. These experiences guide us to quickly find the optimal augmentation combination instead of experimenting with all possible combinations one by one. It should be noted that the empirical researches involved here are not the essential reason for deducing optimal combinations, but for quantitative verification.

For $T_{JC}$ and $T_{CO}$, namely Joint Cutout and trivial Cutout, they both generate several small square zero-value masks/patches in the image. The difference is that the former mainly generates occlusion around the keypoint area of the human body, while the position of the latter is random. Obviously, the former will bring greater challenges to the keypoint prediction task because of more frequent occlusion. The same explanation applies to $T_{JO}$ and $T_{CM}$, except that these patches of them are cropped from another image instead of using zero values.

Finally, for a newly added augmentation, we only need to repeat the first step, that is, to determine its difficulty ranking among basic augmentations. Then we can get a better augmentation combination according to the three principles proposed. For example, we have verified in Table 6 that the augmentation YOCO can produce synergistic effects with RandAugment or TrivialAugment. Then we can rank it, and recommend new and better augmentation combinations, such as $T_{JOCO+YOCO}$ and $T_{JCCM+YOCO}$. These are essentially similar to the procedures we have demonstrated when selecting out $T_{JOCO}$ and $T_{JCCM}$, so the experiments are not repeated in our main paper due to space limitations and minor significance.

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Continue in the next comment.

Dear Reviewer TvMD,

Thank you for your high recognition, valuable feedback and insightful comments on our paper. We appreciate the opportunity to clarify and address your concerns. All our responses are as follows.

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W1: Effectiveness under a single network

R1: In this paper, we mainly use single-network to demonstrate the efficiency and effectiveness of the proposed optimal augmentations combination and multi-path loss strategies. In the experimental comparison, the results involving a single network are mainly concentrated in Tables 3, 4, and 9. When there is only a single network, our method always outperforms the baseline method, namely PoseCons. Please compare line 4 vs. line 8 in Table 3, and line 2 vs. line 5 in Table 9. Even when the baseline method PoseDual uses dual networks, our single network based results are still much better. Please compare line 5 vs. line 8 in Table 3, lines 2/8/14 vs. lines 5/11/17 in Table 4 and line 3 vs. line 5 in Table 9. These advantages based on a single network clearly demonstrate the effectiveness of our core method design.

When we upgrade the proposed method to a dual network structure, the results are naturally better, indicating that it has become the dominant approach. And compared with SSPCM using triple networks, our dual-network approach still has obvious advantages in most cases, while the single-network approach also shows comparable performance.

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W2: Performance with using ResNet-50

R2: We have shown the comparison results based on ResNet-50 in Table 4, mainly including methods PoseDual, SSPCM and Pseudo-HMs. Actually, for a fair comparison, we follow the experimental settings in these compared methods and keep using ResNet-18 in Table 3 for convenient ablation researches, HRNet-w48 in Table 5 to compare performance limits, and HRNet-w32 in Tables 7 and 8 to highlight the best performance.

In addition, we strongly agree that reporting the comparison results based on the more important ResNet-50 is more convincing than ResNet-18. Therefore, we replaced the backbone in Table 3 with ResNet-50 according to setting S1 and re-conducted the comparative experiments. The results are as follows.

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Method		| Nets.	| 1K    | 5K    | 10K   | All
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Supervised	| 1	| 34.8  | 50.6  | 56.4  | 70.9	
PoseCons	| 1	| 43.1  | 57.2  | 61.8  | ---
PoseDual	| 2	| 48.2  | 61.1  | 65.0  | ---	
SSPCM		| 3	| 49.8  | 61.8  | 65.5  | ---
Ours (Single)	| 1	| 49.3  | 61.4  | 65.2  | ---
Ours (Dual)	| 2	| 51.7  | 62.9  | 66.3  | ---
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As shown in the table, similar to using ResNet-18, our method can still achieve a clear advantage. When using a single network, our method outperforms PoseCons and Posedual, while being comparable to SSPCM. And our dual-network based approach achieves significant advantages. We have updated our paper with these additional results in Appendix A.2.

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W3: Marginal improvement on COCO

R3: In our paper, the settings S2 and S3 are based on labeled COCO train-set and unlabeled COCO wild-set. For setting S2, we validate models in the COCO val-set, and report results in Table 4. Our approach has achieved not-insignificant advantages. Of course, the performance improvement gradually decreases as the number of parameters and sophistication of the backbones used grows (e.g., from ResNet-50 to ResNet-101 and HRNet-w48). This phenomenon is also true for previous compared methods such as PoseCons, PoseDual, SSPCM and Pseudo-HMs.

For setting S3, we validate models in the COCO test-dev, and report results in Table 5. The same explanation applies to this similar phenomena. It should be noted that some SOTA supervised learning methods (e.g., UDP and ViTPose) in Table 5 also achieved similar performance, which indicates that the accuracy on this dataset is suspected to be saturated. These situations actually prevent us from objectively evaluating different methods on it.

As an alternative, we recommend evaluating the pros and cons of different SSL methods by looking at the significant performance differences on human images with low annotation rates (see Table 3) or captured using unconventional fisheye cameras (see Table 9).

For referring similar concerns, please refer to our responses to W2/W3/W6 raised by Reviewer r3Sz.

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We hope these clarifications address your concerns and contribute to a better understanding of our work. We are committed to making the necessary revisions in our paper to reflect these clarifications and enhance its overall quality.