  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.
