**Colorization Transformer** is a probabilistic [colorization](https://paperswithcode.com/method/colorization) model composed only of [axial self-attention blocks](https://paperswithcode.com/method/axial). The main advantages of these blocks are the ability to capture a global receptive field with only two layers and $\mathcal{O}(D\sqrt{D})$ instead of $\text{O}(D^{2})$ complexity. In order to enable colorization of high-resolution grayscale images, the task is decomposed into three simpler sequential subtasks: coarse low resolution autoregressive colorization, parallel color and spatial super-resolution.

For coarse low resolution colorization, a conditional variant of [Axial Transformer](https://paperswithcode.com/method/axial) is applied. The authors leverage the semi-parallel sampling mechanism of Axial Transformers. Finally, fast parallel deterministic upsampling models are employed to super-resolve the coarsely colorized image into the final high resolution output.

**LayoutLMv2** is an architecture and pre-training method for document understanding. The model is pre-trained with a great number of unlabeled scanned document images from the IIT-CDIP dataset, where some images in the text-image pairs are randomly replaced with another document image to make the model learn whether the image and OCR texts are correlated or not. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture, so that the model can fully understand the relative positional relationship among different text blocks.

Specifically, an enhanced Transformer architecture is used, i.e. a multi-modal Transformer asisthe backbone of LayoutLMv2. The multi-modal Transformer accepts inputs of three modalities: text, image, and layout. The input of each modality is converted to an embedding sequence and fused by the encoder. The model establishes deep interactions within and between modalities by leveraging the powerful Transformer layers.

LayoutLMv2

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

Colorization Transformer

The  **Synthesizer** is a model that learns synthetic attention weights without token-token interactions. Unlike [Transformers](https://paperswithcode.com/method/transformer), the model eschews dot product self-attention but also content-based self-attention altogether. Synthesizer learns to synthesize the self-alignment matrix instead of manually computing pairwise dot products. It is transformation-based, only relies on simple feed-forward layers, and completely dispenses with dot products and explicit token-token interactions. 

This new module employed by the Synthesizer is called "Synthetic Attention": a new way of learning to attend without explicitly attending (i.e., without dot product attention or [content-based attention](https://paperswithcode.com/method/content-based-attention)). Instead, Synthesizer generate the alignment matrix independent of token-token dependencies.

Source	Colorization Transformer
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com