Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic
ratings to images whilst image aesthetic captioning (IAC) aims to generate
textual descriptions of the aesthetic aspects of images. In this paper, we
study image AQA and IAC together and present a new IAC method termed
Aesthetically Relevant Image Captioning (ARIC). Based on the observation that
most textual comments of an image are about objects and their interactions
rather than aspects of aesthetics, we first introduce the concept of Aesthetic
Relevance Score (ARS) of a sentence and have developed a model to automatically
label a sentence with its ARS. We then use the ARS to design the ARIC model
which includes an ARS weighted IAC loss function and an ARS based diverse
aesthetic caption selector (DACS). We present extensive experimental results to
show the soundness of the ARS concept and the effectiveness of the ARIC model
by demonstrating that texts with higher ARS's can predict the aesthetic ratings
more accurately and that the new ARIC model can generate more accurate,
aesthetically more relevant and more diverse image captions. Furthermore, a
large new research database containing 510K images with over 5 million comments
and 350K aesthetic scores, and code for implementing ARIC are available at
this https URL.

本文提出了一种名为 ARIC 的新的图像审美相关描述方法，该方法利用文本评论的对象和交互来设计评分标准，能够更准确、更相关和更多样化地描述图像审美。

具有美学意义的图像字幕生成

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment has been a relatively hot topic during the
last decade. Most recently, comments type assessment (aesthetic captions) has
been proposed to describe the general aesthetic impression of an image using
text. In this paper, we propose Aesthetic Attributes Assessment of Images,
which means the aesthetic attributes captioning. This is a new formula of image
aesthetic assessment, which predicts aesthetic attributes captions together
with the aesthetic score of each attribute. We introduce a new dataset named
\emph{DPC-Captions} which contains comments of up to 5 aesthetic attributes of
one image through knowledge transfer from a full-annotated small-scale dataset.
Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on
a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated
large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning
and attention model in a single framework. The experimental results on our
DPC-Captions and PCCD dataset reveal that our method can predict captions of 5
aesthetic attributes together with numerical score assessment of each
attribute. We use the evaluation criteria used in image captions to prove that
our specially designed AMAN model outperforms traditional CNN-LSTM model and
modern SCA-CNN model of image captions.

本文介绍了一种通过文本对图像进行审美属性评估的方法，提出了一种新的图像审美评估公式，即预测每个属性的审美特征标题以及每个属性的审美得分，并介绍了一种新的数据集 DPC-Captions。我们使用特殊设计的 AMAN 模型来预测 5 个审美属性的标题和每个属性的数值评估，证明了我们的方法优于传统的 CNN-LSTM 模型和现代的 SCA-CNN 模型。