With the increasing prevalence of smartphones and websites, Image Aesthetic
Assessment (IAA) has become increasingly crucial. While the significance of
attributes in IAA is widely recognized, many attribute-based methods lack
consideration for the selection and utilization of aesthetic attributes. Our
initial step involves the acquisition of aesthetic attributes from both intra-
and inter-perspectives. Within the intra-perspective, we extract the direct
visual attributes of images, constituting the absolute attribute. In the
inter-perspective, our focus lies in modeling the relative score relationships
between images within the same sequence, forming the relative attribute. Then,
to better utilize image attributes in aesthetic assessment, we propose the
Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both
absolute and relative attributes of images. For absolute attributes, we
leverage multiple absolute-attribute perception modules and an
absolute-attribute interacting network. The absolute-attribute perception
modules are first pre-trained on several absolute-attribute learning tasks and
then used to extract corresponding absolute attribute features. The
absolute-attribute interacting network adaptively learns the weight of diverse
absolute-attribute features, effectively integrating them with generic
aesthetic features from various absolute-attribute perspectives and generating
the aesthetic prediction. To model the relative attribute of images, we
consider the relative ranking and relative distance relationships between
images in a Relative-Relation Loss function, which boosts the robustness of the
UMAAF. Furthermore, UMAAF achieves state-of-the-art performance on TAD66K and
AVA datasets, and multiple experiments demonstrate the effectiveness of each
module and the model's alignment with human preference.

在智能手机和网站日益普及的背景下，图像美学评估变得越来越重要。本研究提出了一种统一的多属性美学评估框架（UMAAF），以模拟图像的绝对属性和相对属性。UMAAF 在图像绝对属性方面使用多个模块和交互网络来提取属性特征，并通过相对属性的相对关系损失函数进一步优化模型的鲁棒性。在多个实验证明了各模块的有效性和与人类偏好一致性之后，UMAAF 在 TAD66K 和 AVA 数据集上取得了最先进的性能。