Methods › Computer Vision › Image Colorization Models › Colorization Transformer
Colorization Transformer
Introduced by Manoj Kumar et al. in Colorization Transformer
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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(D√(D)) instead of O(D²) 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.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Colorization Transformer 8 Feb 2021 · 2 repositories · arXiv:2102.04432
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Colorization | 1 |
| Image Colorization | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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