\begin{table}[t]
\caption{The improvement of AP on MPII validation set when our proposed method is applied to various baselines.}
\footnotesize
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{l|l|l|c|lccccccc}
\hline
Method & Venue & Backbone &Input size & Mean & Hea & Sho & Elb & Wri & Hip & Kne & Ank \\
\hline
Integral Pose Regression\cite{sun2018integral} & ECCV~2018  & ResNet-101 &$256\times 256$ &87.9&-&-&-&-&-&-&-\\
UDP\cite{huang2020devil} & CVPR~2020& HRNet-W32 &$256\times256$ &90.4&97.4&96.0&91.0&86.5&89.1&86.6&83.3\\
DarkPose\cite{zhang2020distribution} & CVPR~2020 & HRNet-W32   &$256\times256$ &90.6&97.2&95.9&91.2&86.7&89.7&86.7&84.0\\
TokenPose\cite{li2021tokenpose} & ICCV~2021& TokenPose-L/D6 & $256\times256$  &90.1&97.1&95.9&91.0&85.8&89.5&86.1&82.7 \\
TokenPose\cite{li2021tokenpose} & ICCV~2021& TokenPose-L/D12 & $256\times256$  &90.1&97.2&95.8&90.7&85.9&89.2&86.2&82.3 \\
TokenPose\cite{li2021tokenpose} & ICCV~2021& TokenPose-L/D24 & $256\times256$  &90.2&97.1&95.9&90.4&86.0&89.3&87.1&82.5 \\
Removing Bias\cite{gu2021removing} & ICCV~2021& ResNet-152   &$256\times256$ & 89.9 & - & - & - & - & -& - & -\\
Removing Bias\cite{gu2021removing} & ICCV~2021& HRNet-W32   &$256\times256$ & 90.6 & - & - & - & - & -& - & -\\
\hline
Simple Baseline\cite{xiao2018simple}& ECCV~2018 & ResNet-152   &$256\times256$ & 89.6 & \textbf{97.0} & 95.9 & 90.0 & 85.0 & \textbf{89.2} & 85.3 & 81.3  \\
\textbf{+ Ours} && ResNet-152   &$256\times256$ & \textbf{90.3(\textuparrow0.7)} & \textbf{97.0} & \textbf{96.1} & \textbf{90.6} & \textbf{86.1} & \textbf{89.2} & \textbf{86.7} & \textbf{83.1} \\
\hline
HRNet\cite{sun2019deep} & CVPR~2019& HRNet-W32 &$256\times256$ & 90.4 & 97.1 & 95.9 & 90.7 & 86.1 & 89.4 & 86.9 & 83.2 \\
\textbf{+ Ours}& & HRNet-W32 &$256\times256$ & \textbf{90.9(\textuparrow0.5)} & \textbf{97.3} & \textbf{96.2} & \textbf{91.2} & \textbf{86.8} & \textbf{90.1} & \textbf{87.4} & \textbf{84.1} \\
\hline
HRNet\cite{sun2019deep} & CVPR~2019& HRNet-W48 &$256\times256$ & 90.5 & 96.9 & 96.0 & 90.9 & 86.2 & 89.6 & 87.1 & 83.5 \\
\textbf{+ Ours} && HRNet-W48 &$256\times256$ & \textbf{90.9(\textuparrow0.4)} & \textbf{97.1} & \textbf{96.3} & \textbf{91.2} & \textbf{87.0} & \textbf{90.2} & \textbf{87.5} & \textbf{84.2}\\
\hline
\end{tabular}}
\label{Tab:mpii}
\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}

\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.

\subsection{MPII Human Pose Estimation}
\noindent\textbf{Dataset \& evaluation metric.} The MPII dataset \cite{andriluka20142d} contains around 25K images and more than 40k person instances, which are annotated with 16 body joints. We adopt the standard train/val split in \cite{andriluka20142d} to build the MPII training set and validation set, and conduct all the experiments on this dataset via first training the model on the MPII training set, and then evaluating it on the MPII validation set. Following \cite{sun2019deep}, we use the head-normalized probability of correct keypoint (PCKh) \cite{andriluka20142d} score as the evaluation metric on this dataset and report the PCKh@0.5 score.

\noindent\textbf{Results on the MPII validation set.} In Tab.~\ref{Tab:mpii}, we report the results on the MPII validation set. As shown, applying our proposed method on various baselines results in a consistent performance improvement, which demonstrates the effectiveness of our proposed method.
{"value": "87.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "Integral Pose Regression", "model settings": {"Backbone": "ResNet-101", "Input size": "256\\times 256"}} {"value": "90.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "97.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "96.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "91.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "89.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "83.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "UDP", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "97.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "95.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "91.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "89.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "84.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "DarkPose", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "97.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "95.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "91.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "85.8", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "89.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "86.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "82.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D6", "Input size": "256\\times256"}} {"value": "90.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "97.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "95.8", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "90.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "85.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "89.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "86.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "82.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D12", "Input size": "256\\times256"}} {"value": "90.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "TokenPose", "model settings": {"Backbone": "TokenPose-L/D24", "Input size": "256\\times256"}} {"value": "97.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "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\\times256"}} {"value": "95.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "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\\times256"}} {"value": "90.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "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\\times256"}} {"value": "86.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "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\\times256"}} {"value": "89.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "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\\times256"}} {"value": "87.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "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\\times256"}} {"value": "82.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "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\\times256"}} {"value": "89.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "Removing Bias", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "90.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "Removing Bias", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "89.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "97.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "95.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "90.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "85.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "89.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "85.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "81.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "90.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "Simple Baseline + Ours", "model settings": {"Backbone": "ResNet-152", "Input size": "256\\times256"}} {"value": "97.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "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": "256\\times256"}} {"value": "96.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "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": "256\\times256"}} {"value": "90.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "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": "256\\times256"}} {"value": "86.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "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": "256\\times256"}} {"value": "89.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "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": "256\\times256"}} {"value": "86.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "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": "256\\times256"}} {"value": "83.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "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": "256\\times256"}} {"value": "90.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "97.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "95.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.7", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "89.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "83.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "97.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "96.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "91.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "86.8", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "87.4", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "84.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W32", "Input size": "256\\times256"}} {"value": "90.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "96.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "96.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "90.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "86.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "89.6", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "87.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "83.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "90.9", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP Mean", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "97.1", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hea", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "96.3", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Sho", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "91.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Elb", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "87.0", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Wri", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "90.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Hip", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "87.5", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Kne", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}} {"value": "84.2", "type": "Result", "training data/set": "MPII", "test data/set": "MPII validation Ank", "task": ["human pose estimation", "2D human pose estimation"], "metric": "AP", "experimental settings": {"xx": "yy"}, "model": "HRNet + Ours", "model settings": {"Backbone": "HRNet-W48", "Input size": "256\\times256"}}