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 $\mathcal{O}(D\sqrt{D})$ 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 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.
Source: Colorization TransformerPaper | Code | Results | Date | Stars |
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Component | Type |
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Axial Attention
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Image Model Blocks | |
Linear Layer
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Feedforward Networks | |
Softmax
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Output Functions |