**PixelShuffle** is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of $1/r$. Specifically it rearranges elements in a tensor of shape $(\*, C \times r^2, H, W)$ to a tensor of shape $(\*, C, H \times r, W \times r)$.

Image Source: [Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement](https://www.researchgate.net/figure/The-pixel-shuffle-layer-transforms-feature-maps-from-the-LR-domain-to-the-HR-image_fig3_339531308)

The **Sinkhorn Transformer** is a type of [transformer](https://paperswithcode.com/method/transformer) that uses [Sparse Sinkhorn Attention](https://paperswithcode.com/method/sparse-sinkhorn-attention) as a building block. This component is a plug-in replacement for dense fully-connected attention (as well as local attention, and sparse attention alternatives), and allows for reduced memory complexity as well as sparse attention.

Sinkhorn Transformer

Sparse Sinkhorn Attention

PixelShuffle

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

**UNIMO** is a multi-modal pre-training architecture that can effectively adapt to both single modal and multimodal understanding and generation tasks. UNIMO learns visual representations and textual representations simultaneously, and unifies them into the same semantic space via [cross-modal contrastive learning](https://paperswithcode.com/method/cmcl) (CMCL) based on a large-scale corpus of image collections, text corpus and image-text pairs. The CMCL aligns the visual representation and textual representation, and unifies them into the same semantic
space based on image-text pairs.

Source	Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com