Image Aesthetics Assessment Using Graph Attention Network
This work improves aesthetic evaluation for photography and digital media, though it is incremental in adapting graph neural networks to an existing task.
The paper tackles the problem of image aesthetics assessment by addressing aspect ratio distortion and limited spatial layout modeling in traditional convolutional frameworks, achieving state-of-the-art results in aesthetic score regression on the AVA benchmark.
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.