A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction

TL;DR

This paper introduces a novel neural network model inspired by human peripheral and foveal vision to improve image aesthetic assessment by capturing both broad scene context and fine details.

Contribution

It proposes a double-subnet CNN with gated fusion that mimics human visual processing for more accurate aesthetic prediction.

Findings

Outperforms existing methods on AVA and Photo.net datasets.

Effective in aesthetic quality classification, score regression, and distribution prediction.

Demonstrates the importance of peripheral-foveal inspired architecture in aesthetic assessment.

Abstract

Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vision and peripheral vision. Fovea has the highest possible visual acuity and is responsible for seeing the details. The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea. Inspired by these observations, we propose a Gated Peripheral-Foveal Convolutional Neural Network (GPF-CNN). It is a dedicated double-subnet neural network, i.e. a peripheral subnet and a foveal subnet. The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions. The latter aims to extract fine-grained features on these key regions. Considering that the peripheral vision and foveal vision play different roles in processing different visual stimuli, we further employ a gated information fusion (GIF) network to weight their contributions. The weights are determined through the fully connected layers followed by a sigmoid function. We conduct comprehensive experiments on the standard AVA and Photo.net datasets for unified aesthetic prediction tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction. The experimental results demonstrate the effectiveness of the proposed method.