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。

UMAAF：透过图像的多样属性揭示美学

UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images

Automatic photo aesthetic assessment is a challenging artificial intelligence
task. Existing computational approaches have focused on modeling a single
aesthetic score or a class (good or bad), however these do not provide any
details on why the photograph is good or bad, or which attributes contribute to
the quality of the photograph. To obtain both accuracy and human interpretation
of the score, we advocate learning the aesthetic attributes along with the
prediction of the overall score. For this purpose, we propose a novel multitask
deep convolution neural network, which jointly learns eight aesthetic
attributes along with the overall aesthetic score. We report near human
performance in the prediction of the overall aesthetic score. To understand the
internal representation of these attributes in the learned model, we also
develop the visualization technique using back propagation of gradients. These
visualizations highlight the important image regions for the corresponding
attributes, thus providing insights about model's representation of these
attributes. We showcase the diversity and complexity associated with different
attributes through a qualitative analysis of the activation maps.

提出了一种基于深度卷积神经网络的多任务自动照片美学评估方法，在预测总体美学评分的同时联合学习了八个美学属性，并通过梯度反向传播可视化方法，展示了学习到的模型所代表的属性的重要图像区域。