\begin{table}
\caption{The improvement of AP on COCO validation set when our proposed method is applied to various baselines.}
\footnotesize
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{l|l|l|c|lccccc}
\hline
Method & Venue & Backbone &Input size & AP & $\text{AP}^{50}$ & $\text{AP}^{75}$ & $\text{AP}^{\text{M}}$ &$\text{AP}^{\text{L}}$ &AR  \\
\hline
Hourglass\cite{newell2016stacked}& ECCV~2016 & 8-Stage Hourglass &$256\times192$ &66.9 & - & - & - & - & -\\
CPN\cite{chen2018cascaded}& CVPR~2018 & ResNet-50 &$256\times192$ &69.4 & - & - & - & - & -\\
CPN\cite{chen2018cascaded}& CVPR~2018 & ResNet-50 &$384\times288$ &71.6 & - & - & - & - & -\\
DarkPose\cite{zhang2020distribution}& CVPR~2020 & HRNet-W32   &$384\times288$ &76.6&90.7&82.8&72.7&83.9&81.5\\
UDP\cite{huang2020devil}& CVPR~2020 & HRNet-W32 &$384\times288$ &77.8&91.7&84.5&74.2&84.3&82.4\\
UDP\cite{huang2020devil}& CVPR~2020 & HRNet-W48 &$384\times288$ &77.8&92.0&84.3&74.2&84.5&82.5\\
TokenPose\cite{li2021tokenpose}& ICCV~2021 & TokenPose-L/D24 & $256\times192$ & 75.8 &90.3 &82.5 &72.3 &82.7 &80.9\\
Removing Bias\cite{gu2021removing}& ICCV~2021 & ResNet-152   &$384\times288$ & 74.4 & - & - & - & - & -\\
Removing Bias\cite{gu2021removing}& ICCV~2021 & HRNet-W32   &$256\times192$ & 75.8 & - & - & - & - & -\\
\hline
Simple Baseline\cite{xiao2018simple} & ECCV~2018 & ResNet-152   &$384\times288$ & 75.0 & 90.8 & 82.1 & 67.8 & 78.3 & 80.0 \\
\textbf{+ Ours} & & ResNet-152   &$384\times288$ & \textbf{76.7(\textuparrow 1.7)} & \textbf{92.1} & \textbf{83.6} & \textbf{69.7} & \textbf{80.0} & \textbf{81.3}\\
\hline
HRNet\cite{sun2019deep} & CVPR~2019 & HRNet-W32 &$384\times288$ &76.7&91.9&83.6&73.2&83.2&81.6 \\
\textbf{+ Ours} & & HRNet-W32 &$384\times288$ & \textbf{78.2(\textuparrow1.5)} & \textbf{92.2} & \textbf{84.5} & \textbf{74.3} & \textbf{84.7} & \textbf{82.5}\\
\hline
HRNet\cite{sun2019deep} & CVPR~2019 & HRNet-W48 &$384\times288$ &77.1&91.8&83.8&73.5&83.5&81.8\\
\textbf{+ Ours} & & HRNet-W48 &$384\times288$ & \textbf{78.8(\textuparrow1.7)} & \textbf{92.5} & \textbf{85.1} & \textbf{75.0} & \textbf{85.3} & \textbf{83.1}\\
\hline
HRFormer\cite{YuanFHLZCW21} & NIPS~2021 & HRFormer-Base &$384\times288$ &78.0&92.2&84.8& 74.3 & 84.6 &82.6\\
\textbf{+ Ours} & & HRFormer-Base &$384\times288$ & \textbf{78.9(\textuparrow0.9)} & \textbf{92.6} & \textbf{85.4} & \textbf{75.3} & \textbf{85.3} & \textbf{83.3}\\
\hline
\end{tabular}}
\label{Tab:coco_val}
\end{table}
The Earth Mover's Distance is a 
a technique used for measuring the difference between two probability distributions, which can be understood as the optimal cost needed to transport the mass from one distribution to another. Specifically, for calculating the Earth Mover's Distance, we regard the source distribution as a set of ($N$) suppliers $S = (s_1, ..., s_N)^\top$, where $s_n$ represents the total units of mass that the $n$-th supplier has, and we regard the target distribution as a set of ($M$) demanders $D = (d_1, ..., d_M)^\top$, where $d_m$ represents the total units of mass that the $m$-th demander requires. Besides, we also denote $C \in  R^{N \times M}_{\geq 0}$ as the cost function between the source and target distributions, where $C_{n,m}$ represents the cost for transporting a unit of mass from the $n$-th supplier to the $m$-th demander. Then we aim to find a least-cost transportation plan from the set of possible plans $P = \{p \in R^{N \times M}_{\geq 0}:~p\textbf{1} = S~\&~p^\top\textbf{1} = D\}$ to transport all mass from the $N$ suppliers to the $M$ demanders, where \textbf{1} represents a vector of ones. The Earth Mover's Distance $E_C(S, D)$ denotes the cost of the least-cost transportation plan, which can be formulated as:

\begin{equation}\label{eq:pre_2}
\begin{aligned}
E_C^{reg}(S, D)~=~\langle C,p^{reg} \rangle
~\textbf{where}~ p^{reg}~=~\mathop{\arg\min}_{p \in P}\quad  \left[\langle C,p \rangle - \frac{1}{\lambda} h(p)\right]
\end{aligned}
\end{equation}

\noindent\textbf{The demanders $D$.} As for the demanders, we aim to construct $D$ w.r.t. each body joint to represent its corresponding GT dot annotation.
To achieve this goal, for each body joint, a naive formulation of $D$ is to identify the pixel containing the GT dot annotation (i.e., the upper left pixel in Fig.~\ref{fig:demander}(a)) and construct a single demander (i.e., the yellow dot in Fig.~\ref{fig:demander}(a)) at the center of this pixel.
However, this naive formulation can result in a suboptimal model performance, as the demander formulated in this way can be noticeably different from what the GT dot annotation might suggest.
Generally, the predicted heatmaps outputted by most of the existing \textit{heatmap-based methods} \cite{tompson2014joint,newell2016stacked,xiao2018simple,sun2019deep,li2019rethinking,cheng2020higherhrnet,zhang2020distribution,yang2020transpose,li2021tokenpose,luo2021rethinking,YuanFHLZCW21} have a lower resolution compared to the input image. For example, for the method HRNet \cite{sun2019deep}, when the size of the input image is $384\times288$, the size of the predicted heatmap is $96\times72$ only. Due to such a resolution gap, as shown in  Fig.~\ref{fig:demander}(a), there can exist a non-negligible distance between the location of the demander and the location of the dot annotation, which can affect the performance of the pose estimation model.

\begin{equation}\label{eq:demander}
\begin{aligned}
D = (d_1, d_2, d_3, d_4) ,~\textbf{where}~ d_i = \frac{(g-|x_{dot} - x_i|) \times (g-|y_{dot} - y_i|)}{g^2}
\end{aligned}
\end{equation}

\noindent\textbf{The cost function $C$.} 
To measure the distribution difference between the $H \times W$ suppliers ($S$) constructed from the predicted heatmap and the $4$ demanders ($D$) constructed from the GT dot annotation via calculating their Earth Mover's Distance, we also need to formulate a cost function $C \in  R^{HW \times 4}_{\geq 0}$. 
Denote the coordinates of the $n$-th supplier as $(x_{s_n}, y_{s_n})$, where $n \in \{1, ..., H \times W\}$, and the coordinates of the $m$-th demander as $(x_{d_m}, y_{d_m})$, where $m \in \{1, 2, 3, 4\}$. We here simply formulate $C_{n,m}$, the cost per unit transported from the $n$-th supplier to the $m$-th demander, as the L2 distance between the $n$-th supplier and the $m$-th demander, i.e., $C_{n,m} = \sqrt{(x_{d_m} - x_{s_n})^2 + (y_{d_m} - y_{s_n})^2}$. Using such a cost function, our method can optimize the model directly towards accurately localizing the dot annotation of the body joint via minimizing the distribution difference between the suppliers $S$ and the demanders $D$.

We denote $K$ the number of joints per input image $I$. Then we denote $\mathbf{H_{dot}} = \{H_{dot}^1, ..., H_{dot}^K\}$, $\mathbf{H_{Gau}} = \{H_{Gau}^1, ..., H_{Gau}^K\}$,  and $\mathbf{H_{pred}} = \{H_{pred}^1, ..., H_{pred}^K\}$ respectively the corresponding $K$ GT dot-annotated heatmaps, GT Gaussian-smoothed heatmaps, and predicted heatmaps of the input image $I$. We denote $\mathcal D_{dot} = \{(I, \mathbf{H_{dot}})\}$ the joint distribution of the input image and the corresponding $K$ dot-annotated heatmaps, and $\mathcal D_{Gau} = \{(I, \mathbf{H_{Gau}})\}$ the joint distribution of the input image and the corresponding $K$ Gaussian-smoothed heatmaps. Besides, we denote $l_{MSE}(a, b) = \left\| a - b\right\|_2^2$ the pixel-wise MSE loss, and $\phi$ the model parameters where $\phi(I) = \mathbf{H_{pred}}$. After that, we denote $R(\mathcal D_{dot}, \phi, l_{MSE}) = \mathbb{E}_{(I, \mathbf{H_{dot}}) \sim \mathcal D_{dot}}[l_{MSE}(\phi(I), \mathbf{H_{dot}})]$ as the expected risk calculated between the predicted heatmaps and the GT dot-annotated heatmaps, and $R(\mathcal D_{Gau}, \phi, l_{MSE}) = \mathbb{E}_{(I, \mathbf{H_{Gau}}) \sim \mathcal D_{Gau}}[l_{MSE}(\phi(I), \mathbf{H_{Gau}})]$ as the expected risk calculated between the predicted heatmaps and the Gaussian-smoothed heatmaps.

\subsection{COCO Keypoint Detection}
\noindent\textbf{Dataset \& evaluation metric.} The COCO dataset \cite{lin2014microsoft} contains more than 200k images and 250k person instances, which are annotated with 17 body joints. This dataset has three subsets including COCO training set, COCO validation set, and COCO test-dev set, which have 57k, 5k and 20k images, respectively. We conduct experiments on this dataset via first training the model on the train2017 set, and then evaluating the model on the val2017 set and test-dev2017 set. Following \cite{xiao2018simple,sun2019deep,YuanFHLZCW21}, we use standard average precision (AP) calculated based on Object Keypoint Similarity (OKS) to evaluate model performance.

\noindent\textbf{Results.} In Tab.~\ref{Tab:coco_val} and Tab.~\ref{Tab:coco_test_dev}, we report results on the COCO validation and test-dev sets. We observe that after applying our method on various baselines, a significant performance enhancement is achieved, which shows effectiveness of our proposed method. Moreover, we compare our method with other state-of-the-art 2D human pose estimation methods. Our method achieves superior performance compared to these methods, further demonstrating the effectiveness of our  method.
{"value": "66.9", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Hourglass", "model settings": {"Backbone": "8-Stage Hourglass", "Input size": "256\\times192"}} {"value": "69.4", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "CPN", "model settings": {"Backbone": "ResNet-50", "Input size": "256\\times192"}} {"value": "71.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "CPN", "model settings": {"Backbone": "ResNet-50", "Input size": "384\\times288"}} {"value": "76.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "90.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "82.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "72.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "83.9", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "81.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "77.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "91.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "84.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "74.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "84.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "82.4", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "77.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "92.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "84.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "74.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "84.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "82.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "75.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "90.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "82.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "72.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "82.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "80.9", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times192"}} {"value": "74.4", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Removing Bias", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "75.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Removing Bias", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times192"}} {"value": "75.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "90.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "82.1", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "67.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "78.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "80.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "76.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "92.1", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "83.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "69.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "80.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "81.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "384\\times288"}} {"value": "76.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "91.9", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "83.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "73.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "83.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "81.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "78.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "92.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "84.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "74.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "84.7", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "82.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "384\\times288"}} {"value": "77.1", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "91.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "83.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "73.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "83.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "81.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "78.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "92.5", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "85.1", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "75.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "85.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "83.1", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "384\\times288"}} {"value": "78.0", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "92.2", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "84.8", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "74.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "84.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "82.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRFormer", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "78.9", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "92.6", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{50}", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "85.4", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{75}", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "75.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{M}", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "85.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "\\text{AP}^{\\text{L}}", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}} {"value": "83.3", "type": "Result", "training data/set": "COCO", "test data/set": "COCO validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AR", "experimental settings": {"xx": "yy"}, "model": "HRFormer + Ours", "model settings": {"Backbone": "HRFormer-Base", "Input size": "384\\times288"}}