Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an “average” user. However, such generic aesthetics models neglect the fact that users’ aesthetic preferences vary significantly depending on their unique preferences. Therefore, it is essential to tackle the issue for personalized IAA (PIAA). Since PIAA is a typical small sample learning (SSL) problem, existing PIAA models are usually built by fine-tuning the well-established generic IAA (GIAA) models, which are regarded as prior knowledge. Nevertheless, this kind of prior knowledge based on “average aesthetics” fails to incarnate the aesthetic diversity of different people. In order to learn the shared prior knowledge when different people judge aesthetics, that is, learn how people judge image aesthetics, we propose a PIAA method based on meta-learning with bilevel gradient optimization (BLG-PIAA), which is trained using individual aesthetic data directly and generalizes to unknown users quickly. The proposed approach consists of two phases: 1) meta-training and 2) meta-testing. In meta-training, the aesthetics assessment of each user is regarded as a task, and the training set of each task is divided into two sets: 1) support set and 2) query set. Unlike traditional methods that train a GIAA model based on average aesthetics, we train an aesthetic meta-learner model by bilevel gradient updating from the support set to the query set using many users’ PIAA tasks. In meta-testing, the aesthetic meta-learner model is fine-tuned using a small amount of aesthetic data of a target user to obtain the PIAA model. The experimental results show that the proposed method outperforms the state-of-the-art PIAA metrics, and the learned prior model of BLG-PIAA can be quickly adapted to unseen PIAA tasks.

中文翻译：

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通过双级梯度优化元学习的个性化图像美学评估

典型的图像美学评估 (IAA) 是针对“普通”用户感知的通用美学建模的。然而，这样的通用美学模型忽略了用户的审美偏好根据他们的独特偏好而显着变化的事实。因此，必须解决个性化 IAA (PIAA) 的问题。由于 PIAA 是一个典型的小样本学习 (SSL) 问题，现有的 PIAA 模型通常是通过对公认的通用 IAA (GIAA) 模型进行微调来构建的，这些模型被视为先验知识。然而，这种基于“平均审美”的先验知识并不能体现不同人群的审美多样性。为了学习不同人判断美学时共享的先验知识，即学习人们如何判断图像美学，我们提出了一种基于具有双层梯度优化的元学习 (BLG-PIAA) 的 PIAA 方法，该方法直接使用个人审美数据进行训练，并快速推广到未知用户。所提出的方法包括两个阶段：1）元训练和2）元测试。在元训练中，将每个用户的审美评估视为一个任务，每个任务的训练集分为两组：1）支持集和2）查询集。与基于平均美学训练 GIAA 模型的传统方法不同，我们使用许多用户的 PIAA 任务通过从支持集到查询集的双层梯度更新来训练美学元学习器模型。在元测试中，审美元学习者模型使用目标用户的少量审美数据进行微调以获得PIAA模型。