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。

深度 CNN 学习摄影美学

Learning Photography Aesthetics with Deep CNNs

Deep convolutional neural networks (CNN) have recently been shown to generate
promising results for aesthetics assessment. However, the performance of these
deep CNN methods is often compromised by the constraint that the neural network
only takes the fixed-size input. To accommodate this requirement, input images
need to be transformed via cropping, warping, or padding, which often alter
image composition, reduce image resolution, or cause image distortion. Thus the
aesthetics of the original images is impaired because of potential loss of fine
grained details and holistic image layout. However, such fine grained details
and holistic image layout is critical for evaluating an image's aesthetics. In
this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional
Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This
novel scheme is able to accept arbitrary sized images, and learn from both
fined grained details and holistic image layout simultaneously. To enable
training on these hybrid inputs, we extend the method by developing a dedicated
double-subnet neural network structure, i.e. a Multi-Patch subnet and a
Layout-Aware subnet. We further construct an aggregation layer to effectively
combine the hybrid features from these two subnets. Extensive experiments on
the large-scale aesthetics assessment benchmark (AVA) demonstrate significant
performance improvement over the state-of-the-art in photo aesthetic
assessment.

本文介绍了一种新的自适应布局感知多补丁卷积神经网络 (A-Lamp CNN) 结构，用于照片美学评估，该网络结构能够同时学习细节和完整的图像布局。通过在大规模美学评估基准 (AVA) 上进行广泛实验，证明了在照片美学评估方面比现有技术有显着的性能提高。