AACP: Aesthetics Assessment of Children's Paintings Based on Self-Supervised Learning

ArXiv Pub Date : 2024-03-12 DOI:10.1609/aaai.v38i3.28030
Shiqi Jiang, Ning Li, Chen Shi, Liping Guo, Changbo Wang, Chenhui Li
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Abstract

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.
AACP: 基于自我监督学习的儿童绘画美学评估
儿童绘画美学评估(AACP)是图像美学评估(IAA)的一个重要分支,在儿童教育中发挥着重要作用。这项任务面临着独特的挑战,例如可用数据有限以及需要从多个角度进行评估度量。然而,以前的方法依赖于训练大型数据集,然后为图像提供美学评分,这不适用于 AACP。为了解决这个问题,我们构建了一个儿童绘画美学评估数据集和一个基于自监督学习的模型。1) 我们建立了一个由两部分组成的新型数据集:第一部分包含 20k 多张未标记的儿童画图像;第二部分包含 1.2k 张儿童画图像,每张图像包含由多个设计专家标记的 8 个属性。2) 我们设计了一个流水线,包括特征提取模块、感知模块和分离评估模块。3) 我们使用 AACP 数据集进行了定性和定量实验,比较了我们的模型与其他五种方法的性能。实验结果表明,我们的方法可以准确捕捉美学特征,并达到最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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