AACP: Aesthetics assessment of children's paintings based on
  self-supervised learning
Shiqi Jiang Ning Li Chen Shi Liping Guo Changbo Wang Chenhui Li
The Aesthetics Assessment of Children's Paintings (AACP) is an important
branch of the image aesthetics assessment (IAA), playing a significant role in
children's education. This task presents unique challenges, such as limited
available data and the requirement for evaluation metrics from multiple
perspectives. However, previous approaches have relied on training large
datasets and subsequently providing an aesthetics score to the image, which is
not applicable to AACP. To solve this problem, we construct an aesthetics
assessment dataset of children's paintings and a model based on self-supervised
learning. 1) We build a novel dataset composed of two parts: the first part
contains more than 20k unlabeled images of children's paintings; the second
part contains 1.2k images of children's paintings, and each image contains
eight attributes labeled by multiple design experts. 2) We design a pipeline
that includes a feature extraction module, perception modules and a
disentangled evaluation module. 3) We conduct both qualitative and quantitative
experiments to compare our model's performance with five other methods using
the AACP dataset. Our experiments reveal that our method can accurately capture
aesthetic features and achieve state-of-the-art performance.