  Aesthetic quality assessment (AQA) is a challenging task due to complex
aesthetic factors. Currently, it is common to conduct AQA using deep neural
networks that require fixed-size inputs. Existing methods mainly transform
images by resizing, cropping, and padding or employ adaptive pooling to
alternately capture the aesthetic features from fixed-size inputs. However,
these transformations potentially damage aesthetic features. To address this
issue, we propose a simple but effective method to accomplish full-resolution
image AQA by combining image padding with region of image (RoM) pooling.
Padding turns inputs into the same size. RoM pooling pools image features and
discards extra padded features to eliminate the side effects of padding. In
addition, the image aspect ratios are encoded and fused with visual features to
remedy the shape information loss of RoM pooling. Furthermore, we observe that
the same image may receive different aesthetic evaluations under different
themes, which we call theme criterion bias. Hence, a theme-aware model that
uses theme information to guide model predictions is proposed. Finally, we
design an attention-based feature fusion module to effectively utilize both the
shape and theme information. Extensive experiments prove the effectiveness of
the proposed method over state-of-the-art methods.
