Single reference
Deepremaster
Paper: https://github.com/satoshiiizuka/siggraphasia2019_remastering
This model is originally designed for film colorization. To run this benchmark the input image is duplicated 5 times. The reference images are supposed to be colored frames chosen from the movies.
This means that significant differences in the reference images cannot be used, as illustrated below. (‘Recolor source’ vs other rows)
Task | Image #1 | Image #2 | Image #3 | Image #4 |
---|---|---|---|---|
Recolor source | ||||
Inverted reference | ||||
Task | Image #1 | Image #2 | Image #3 | Reference |
Full correspondence | ||||
Full correspondence | ||||
Partial reference | ||||
Partial reference | ||||
Partial source | ||||
Partial source | ||||
Semantic correspondence strong | ||||
Semantic correspondence strong | ||||
Semantic correspondence weak | ||||
Semantic correspondence weak | ||||
Distractors | ||||
Random noise | ||||
Random noise | ||||
Gray | ||||
Gray | ||||
Contemporary | ||||
Contemporary | ||||
Contemporary |
Additional Information
- Last updated: 11 April 2024 09:29
- GPU info: NVIDIA GeForce GTX 1080 Ti 11 GB, Compute Capability 6.1
- CUDA version: 11.7
- PyTorch version: 1.13.1