Day 2: Deep High-Resolution Representation Learning for Human Pose Estimation

最近在做 pose estimation 相关的东西，这篇论文之前其实看过，也做了些笔记，不过没有统一整理。今天没太多时间，所以先把一些重要的东西写下来，记在这。

这篇文章的主要贡献有：

• 以往的 pose estimation 模型，都是有个从高精到低精，再从低精到高精的过程，而且整个过程中是串行的，因此会损失一定的 spatial information，本文提出的 HRnet，采用了并行模式，在整个过程中都保留了高精的特征图。
• 此外，本模型采用了 multi scale fusion，把前一个stage的高、低精度进行了一个融合，再输出到下一个阶段。

以下为英文部分

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Core Ideas and Contributions

1. Maintains high-resolution representations through the whole process.
2. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high resolution representations

Methods and Approaches

General Description

• We start from a high-resolution subnetwork as the fifirst stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the multi-resolution subnetworks in parallel. We conduct repeated multi-scale fusions by exchanging the information across the parallel multi-resolution subnetworks over and over through the whole process.
• We perform repeated multi-scale fusions to boost the high-resolution representations with the help of the low-resolution representations of the same depth and similar level, and vice versa, resulting in that high-resolution representations are also rich for pose estimation.

Detailed Description

• With the input of size $W\times H\times 3$, trasform this problem to estimating $K$ heatmaps of size $W^{\prime }\times H^{\prime }$, {H1,H2,...,HK}\{H_1, H_2, ..., H_K\}{H1​,H2​,...,HK​}, where each heatmap $H_k$ indicates the location confidence of the $k$th keypoint.
• It composed of a stem consisting of two strided convolutions decreasing the resolution, a main body outputting the feature maps with the same resolution as its input feature maps, and a regressor estimating the heatmaps where the keypoint positions are chosen and transformed to the full resolution

Sequencial multi-resolution subnetworks

• Let ${\mathcal{N}}_{sr}$ be the subnetwork in the $s$th stage and $r$ be the resolution index (Its resolution is $\frac{1}{2^{r-1}}$ of the resolution of the first subnetwork). The high-to-low network with $S$ (e.g., 4) stages can be denoted as:

$${\mathcal{N}}_{11}\to {\mathcal{N}}_{22}\to {\mathcal{N}}_{33}\to {\mathcal{N}}_{44}$$

Parallel multi-resolution subnetworks

• start from a high-resolution subnetwork as the first stage.
• gradually add high-to-low subnetworks one by one, forming new stages, and connect the multi-resolution subnetworks in parallel.
• the resulting resolution for the parallel subnetworks of a later stage, consists of the resolutions from the previous stage, and an extra lower one

$$\begin{array}{r}{\mathcal{N}}_{11}\to {\mathcal{N}}_{21}\to {\mathcal{N}}_{31}\to {\mathcal{N}}_{41}\\ \searrow {\mathcal{N}}_{22}\to {\mathcal{N}}_{32}\to {\mathcal{N}}_{42}\\ \searrow {\mathcal{N}}_{33}\to {\mathcal{N}}_{43}\\ \searrow {\mathcal{N}}_{44}.\end{array}$$

Repeated multi-scale fusion

• We introduce exchange units across parallel subnetworks such that each subnetwork repeatedly receives the information from other parallel subnetworks
• We divided the third stage into several (e.g., 3) exchange blocks, and each block is composed of 3 parallel convolution units with an exchange unit across the parallel units, which is given as follows

$$\begin{array}{lllllc}{\mathcal{C}}_{31}^1& \searrow & \nearrow {\mathcal{C}}_{31}^2& \searrow & \nearrow {\mathcal{C}}_{31}^3& \searrow \\ {\mathcal{C}}_{32}^1& \to {\mathcal{E}}_3^1& \to {\mathcal{C}}_{32}^2& \to {\mathcal{E}}_3^2& \to {\mathcal{C}}_{32}^3& \to {\mathcal{E}}_3^3\\ {\mathcal{C}}_{33}^1& \nearrow & \searrow {\mathcal{C}}_{33}^2& \nearrow & \searrow {\mathcal{C}}_{33}^3& \nearrow \end{array}$$

• $C_{sr}^b$ represents the convolution unit in the $r$th resolution of the $b$th block in the $s$th stabe, and ${\mathcal{E}}_s^b$ is the corresponding exchange unit.
• The inputs are $s$ **response maps: {X1,X2,…,Xs}\left\{\mathbf{X}_{1}, \mathbf{X}_{2}, \ldots, \mathbf{X}_{s}\right\}{X1​,X2​,…,Xs​}. The outputs are $s$ **response maps: {Y1,Y2,…,Ys}\left\{\mathbf{Y}_{1}, \mathbf{Y}_{2}, \ldots, \mathbf{Y}_{s}\right\}{Y1​,Y2​,…,Ys​}, whose resolutions and widths are the same to the input. Each output is an aggregation of the input maps, ${\mathbf{Y}}_k={\sum }_{i=1}^{s}a\left({\mathbf{X}}_i,k\right)$. The exchange unit across stages has an extra output map ${\mathbf{Y}}_{s+1}:{\mathbf{Y}}_{s+1}=a\left({\mathbf{Y}}_s,s+1\right)$.
• The function $a\left({\mathbf{X}}_i,k\right)$ consists of upsampling or downsampling $X_i$ **from resolution $i$ **to resolution $k$. We adopt strided 3×3 convolutions for downsampling. For instance, one strided 3×3 convolution with the stride 2 for 2× downsampling, and two consecutive strided 3 × 3 convolutions with the stride 2 for 4× downsampling. For upsampling, we adopt the simple nearest neighbor sampling following a $1\times 1$ convolution for aligning the number of channels. If $i=k,a(\cdot ,\cdot )$ is just an identify connection: $a\left({\mathbf{X}}_i,k\right)={\mathbf{X}}_i$

Network instantiation

• This part is not clear enough just by reading the paper, so I will look into the code and paste a new link here describing the codes in the future.