RED-Aes learns aesthetic changes from edit-induced image pairs and a new RED-20k dataset via three-stage relative ranking training, claiming SOTA generalization over absolute MOS regression.
A strict consensus mechanism was adopted, requiringunanimous agreementamong all three judges to filter out ambiguous or noisy pairs
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Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment
RED-Aes learns aesthetic changes from edit-induced image pairs and a new RED-20k dataset via three-stage relative ranking training, claiming SOTA generalization over absolute MOS regression.