  Aspect ratio and spatial layout are two of the principal factors determining
the aesthetic value of a photograph. But, incorporating these into the
traditional convolution-based frameworks for the task of image aesthetics
assessment is problematic. The aspect ratio of the photographs gets distorted
while they are resized/cropped to a fixed dimension to facilitate training
batch sampling. On the other hand, the convolutional filters process
information locally and are limited in their ability to model the global
spatial layout of a photograph. In this work, we present a two-stage framework
based on graph neural networks and address both these problems jointly. First,
we propose a feature-graph representation in which the input image is modelled
as a graph, maintaining its original aspect ratio and resolution. Second, we
propose a graph neural network architecture that takes this feature-graph and
captures the semantic relationship between the different regions of the input
image using visual attention. Our experiments show that the proposed framework
advances the state-of-the-art results in aesthetic score regression on the
Aesthetic Visual Analysis (AVA) benchmark.
