**CV-MIM**, or **Contrastive Cross-View Mutual Information Maximization**, is a representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization, which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. It further utilizes two regularization terms to ensure disentanglement and smoothness of the learned representations.

**PCA Whitening** is a processing step for image based data that makes input less redundant. Adjacent pixel or feature values can be highly correlated, and whitening through the use of [PCA](https://paperswithcode.com/method/pca) reduces this degree of correlation.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)

PCA Whitening

CV-MIM

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

**Spatially Separable Self-Attention**, or **SSSA**, is an [attention module](https://paperswithcode.com/methods/category/attention-modules) used in the [Twins-SVT](https://paperswithcode.com/method/twins-svt) architecture that aims to reduce the computational complexity of [vision transformers](https://paperswithcode.com/methods/category/vision-transformer) for dense prediction tasks (given high-resolution inputs). SSSA is composed of [locally-grouped self-attention](https://paperswithcode.com/method/locally-grouped-self-attention) (LSA) and [global sub-sampled attention](https://paperswithcode.com/method/global-sub-sampled-attention) (GSA).

Formally, spatially separable self-attention (SSSA) can be written as:

$$
\hat{\mathbf{z}}\_{i j}^{l}=\text { LSA }\left(\text { LayerNorm }\left(\mathbf{z}\_{i j}^{l-1}\right)\right)+\mathbf{z}\_{i j}^{l-1} $$

$$\mathbf{z}\_{i j}^{l}=\mathrm{FFN}\left(\operatorname{LayerNorm}\left(\hat{\mathbf{z}}\_{i j}^{l}\right)\right)+\hat{\mathbf{z}}\_{i j}^{l} $$

$$ \hat{\mathbf{z}}^{l+1}=\text { GSA }\left(\text { LayerNorm }\left(\mathbf{z}^{l}\right)\right)+\mathbf{z}^{l} $$

$$ \mathbf{z}^{l+1}=\text { FFN }\left(\text { LayerNorm }\left(\hat{\mathbf{z}}^{l+1}\right)\right)+\hat{\mathbf{z}}^{l+1}$$

$$i \in\{1,2, \ldots ., m\}, j \in\{1,2, \ldots ., n\}
$$

where LSA means locally-grouped self-attention within a sub-window; GSA is the global sub-sampled attention by interacting with the representative keys (generated by the sub-sampling functions) from each sub-window $\hat{\mathbf{z}}\_{i j} \in \mathcal{R}^{k\_{1} \times k\_{2} \times C} .$ Both LSA and GSA have multiple heads as in the standard self-attention.

Source	Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com