3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in color-manipulation-7
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Use these libraries to find color-manipulation-7 models and implementations
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A new learnable geometric-unrelated rectification, Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network, giving neural networks the ability to actively transform input intensity rather than only the spatial rectification.
A Laplacian Pyramid Translation Network (LPTN) is proposed to simultaneously perform these two tasks, where it is revealed that the attribute transformations, such as illumination and color manipulation, relate more to the low-frequency component, while the content details can be adaptively refined on high-frequency components.
A Neural Implicit LUT (NILUT) is proposed, an implicitly defined continuous 3D color transformation parameterized by a neural network that can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly.
This work proposes NamedCurves, a learning-based image enhancement technique that separates the image into a small set of named colors via tone curves and then combines the images using an attention-based fusion mechanism to mimic spatial editing.
This work aims to develop a neural network architecture that can encode hundreds of LUTs in a single compact representation and shows that minor modifications to the network architecture enable a bijective encoding that produces LUTs that are invertible, allowing for reverse color processing.
Adding a benchmark result helps the community track progress.