Paper: RTMPose: Real-Time Models for Multi-Person Pose Estimation (ICLR-2024)

Reviewer comment (Evaluation):
The author did not analyze why Large Kernel Convolution works. Some heat maps may be helpful for analysis.

Author response:
Regarding your suggestion on using heatmaps for analysis, we appreciate the suggestion. In fact, we did explore heatmap visualization in our experiments. However, it's important to note that SimCC differs from traditional heatmap-based methods, and in the case of Large Kernel Convolution, the feature maps received are not typical heatmaps. The convolutional layer here functions more like a spatial token mixer, making the visualization less interpretable compared to heatmap-based methods.

Reviewer comment (Experiments):
The author did not analyze why NVIDIA GeForce GTX 1660 Ti GPU and Intel I7-11700 CPU were chosen. Has the author tried other devices?

Author response:
We appreciate your inquiry regarding the choice of devices. Our decision to use the NVIDIA GeForce GTX 1660 Ti GPU and Intel I7-11700 CPU was deliberate. Given the focus of our work on designing a lightweight pose estimation model for common industrial scenarios, we opted for lower-cost, industry-common devices. This choice ensures that our results are directly applicable and valuable for industrial deployment.

Reviewer comment (Novelty):
Despite its high performance and efficiency, the proposed method is an integrated engineering framework of existing methods and training tricks, lacks its original methodological contributions, and is not suitable for top academic conferences like ICLR.

Author response:
2. **Contribution Clarification:**
   - Our work originated from the identification of significant redundancies within the SimCC method (consuming 35\% of parameters and 26.7\% of computation while only gaining 0.8 AP). We believe that the simplification of the structure, as explored in our research, holds immense potential in the domain of lightweight pose estimation. Subsequently, we conducted a systematic analysis of five factors influencing real-time multi-person pose estimation and designed a comprehensive real-time pose estimation pipeline based on these insights.
   - While we acknowledge that our proposed method integrates existing modules and training strategies, we believe we are the first to comprehensively address these factors in a single study, making a systematic contribution to the field of lightweight pose estimation. 
   - An important contribution of our work is challenging the prevailing stereotype in the academic community regarding top-down algorithmsWe understand that our innovation is more methodological than algorithmic, and we hope this perspective aligns with your expectations.

Reviewer comment (Writing):
The writing can be improved. For example, some symbols in Equations 1 and 2 are not defined, which should not be ignored for an academic paper.

Author response:
1. **Presentation Quality:**
   We have thoroughly reviewed the presentation of the paper and addressed the identified issues, including typos and repetitive references. Notably, we have supplemented the definitions of symbols in Equations 1 and 2 to ensure clarity and precision in the academic context.

Reviewer comment (Writing):
The writing can be improved. For example, some references have no journal or conference information (e.g., Huang 2020c, Li 2021c, Lyu 2022, etc.).

Author response:
1. **Presentation Quality:**
   We have thoroughly reviewed the presentation of the paper and addressed the identified issues, including typos and repetitive references. Notably, we have supplemented the definitions of symbols in Equations 1 and 2 to ensure clarity and precision in the academic context.

Reviewer comment (Writing):
Typos. On page 4, "Table 3.1" should be "Table 1".

Author response:
1. **Presentation Quality:**
   We have thoroughly reviewed the presentation of the paper and addressed the identified issues, including typos and repetitive references. Notably, we have supplemented the definitions of symbols in Equations 1 and 2 to ensure clarity and precision in the academic context.

Reviewer comment (Writing):
The presentation of the paper is not good. There are a lot of typos and repetitive references. e.g., UDP (Huang et al., 2020), Crowdpose (Li et al., 2018); Typos: Table 3.1?

Author response:
1. **Presentation Quality**:
    We have thoroughly reviewed the presentation of the paper and addressed the identified issues, including typos and repetitive references.

Reviewer comment (Evaluation):
Although experiments are extensively done, there is no interesting insight into the five factors, which seems hyper-parameter tuning to me.

Author response:
2. **Contribution Refinement**:
    In response to your comments on the contribution section, we have refined our statements to better highlight the unique aspects of our work:
    - We have conducted simple yet insightful experiments to demonstrate the presence of redundant components in the SimCC method (consuming 35\% of parameters and 26.7\% of computation while only gaining 0.8 AP). The identification of these redundancies underscores the substantial potential for a simplified structure in the domain of lightweight pose estimation.
    - An important contribution of our work is challenging the prevailing stereotype in the academic community regarding top-down algorithms. Contrary to the common belief that top-down algorithms sacrifice speed for accuracy, our research establishes that top-down approaches can achieve both high speed and precision, making them an optimal choice for industrial pose estimation algorithm design.
    - Our paper provides a systematic analysis of the five aspects influencing real-time multi-person pose estimation. Additionally, we have designed a comprehensive real-time pose estimation pipeline based on this analysis.