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.

翻译：儿童绘画美学评估（AACP）是图像美学评估（IAA）的重要分支，在儿童教育中发挥着重要作用。该任务面临独特挑战，如可用数据有限，且需从多角度进行评价指标评估。然而，以往方法依赖于训练大型数据集并随后为图像提供美学评分，这并不适用于AACP。为解决此问题，我们构建了一个儿童绘画美学评估数据集及基于自监督学习的模型。1）我们构建了一个由两部分组成的新数据集：第一部分包含超过2万张未标注的儿童绘画图像；第二部分包含1200张儿童绘画图像，每张图像包含由多位设计专家标注的八个属性。2）我们设计了一个包含特征提取模块、感知模块和解耦评估模块的流水线。3）我们使用AACP数据集进行了定性与定量实验，将我们模型的性能与其他五种方法进行比较。实验表明，我们的方法能够准确捕捉美学特征，并达到最优性能。