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

arXiv: https://arxiv.org/abs/1812.07989 · HTML: https://mlsanity.com/paper/1812.07989/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [Distilling Knowledge from Object Classification to Aesthetics Assessment](https://mlsanity.com/paper/2206.00809/) (2022)
- [Composition and Style Attributes Guided Image Aesthetic Assessment](https://mlsanity.com/paper/2111.04647/) (2021)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)
- [Image Aesthetics Assessment via Learnable Queries](https://mlsanity.com/paper/2309.02861/) (2023)
- [Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study](https://mlsanity.com/paper/2106.03316/) (2021)
- [A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction](https://mlsanity.com/paper/2010.07661/) (2020)
- [Exploring CNN-based models for image's aesthetic score prediction with using ensemble](https://mlsanity.com/paper/2210.05119/) (2022)
- [MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction](https://mlsanity.com/paper/2311.13929/) (2023)
- [Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning](https://mlsanity.com/paper/2205.03574/) (2022)

## Abstract (arXiv)

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.