Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment

Dongyu She

Abstract:

have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is
a challenging topic by its subjective nature, psychological
studies have confirmed a strong correlation between image layouts and perceived image quality. While previous
state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs),
our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more
suited for modeling complex relations among image regions
than vanilla convolutional layers. Specifically, we present a
Hierarchical Layout-Aware Graph Convolutional Network
(HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LAGCN modules. The first LA-GCN module constructs an
aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN
module performs graph reasoning after aggregating signifi-
cant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both
spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model
outperforms the state-of-the-art on the AVA and AADB
datasets across three different tasks.