通过任务向量定制扩展个性化审美评估

Scaling Up Personalized Aesthetic Assessment via Task Vector Customization

July 9, 2024

作者: Jooyeol Yun, Jaegul Choo

cs.AI

摘要

个性化图像美学评估的任务旨在通过少量用户提供的输入，定制美学评分预测模型以符合个人偏好。然而，当前方法的可扩展性和泛化能力受到昂贵策划数据库的限制相当大。为了克服这一长期存在的可扩展性挑战，我们提出了一种独特的方法，利用现成的数据库进行通用图像美学评估和图像质量评估。具体而言，我们将每个数据库视为一个独特的图像评分回归任务，展示了不同程度的个性化潜力。通过确定代表每个数据库特定特征的任务向量的最佳组合，我们成功地为个体创建了个性化模型。这种集成多个模型的方法使我们能够利用大量数据。我们广泛的实验表明了我们的方法在泛化到以前未见领域方面的有效性-这是以前方法一直难以实现的挑战，使其在实际场景中具有高度适用性。我们的新方法通过为个性化美学评估提供可扩展的解决方案并为未来研究建立高标准，显著推进了该领域。

English

The task of personalized image aesthetic assessment seeks to tailor aesthetic score prediction models to match individual preferences with just a few user-provided inputs. However, the scalability and generalization capabilities of current approaches are considerably restricted by their reliance on an expensive curated database. To overcome this long-standing scalability challenge, we present a unique approach that leverages readily available databases for general image aesthetic assessment and image quality assessment. Specifically, we view each database as a distinct image score regression task that exhibits varying degrees of personalization potential. By determining optimal combinations of task vectors, known to represent specific traits of each database, we successfully create personalized models for individuals. This approach of integrating multiple models allows us to harness a substantial amount of data. Our extensive experiments demonstrate the effectiveness of our approach in generalizing to previously unseen domains-a challenge previous approaches have struggled to achieve-making it highly applicable to real-world scenarios. Our novel approach significantly advances the field by offering scalable solutions for personalized aesthetic assessment and establishing high standards for future research. https://yeolj00.github.io/personal-projects/personalized-aesthetics/