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Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment
In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets.
A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction
In this paper, we propose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predict aesthetic score distribution from images.
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