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What is: Colorization Transformer?

SourceColorization Transformer
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

Colorization Transformer is a probabilistic colorization model composed only of axial self-attention blocks. The main advantages of these blocks are the ability to capture a global receptive field with only two layers and O(DD)\mathcal{O}(D\sqrt{D}) instead of O(D2)\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 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.