arXiv:2202.08143
공식 코드 · Papers with Code
Bias in Automated Image Colorization: Metrics and Error Types
원문 초록
We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturation effect, and also provide novel observations: a shift towards the training average, a pervasive blue shift, different color shifts among image categories, and a manual categorization of colorization errors in three classes.
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Leveraging the Powerful Attention of a Pre-trained Diffusion Model for Exemplar-based Image Colorization
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery
Language-based Image Colorization: A Benchmark and Beyond
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation
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