# Composition and Style Attributes Guided Image Aesthetic Assessment

arXiv: https://arxiv.org/abs/2111.04647 · HTML: https://mlsanity.com/paper/2111.04647/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Measuring and regularizing networks in function space](https://mlsanity.com/paper/1805.08289/) (2018)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning](https://mlsanity.com/paper/2001.08113/) (2020)
- [A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment](https://mlsanity.com/paper/1704.00248/) (2017)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- [Composition-Aware Image Aesthetics Assessment](https://mlsanity.com/paper/1907.10801/) (2019)
- [A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction](https://mlsanity.com/paper/1812.07989/) (2018)
- [Learning Photography Aesthetics with Deep CNNs](https://mlsanity.com/paper/1707.03981/) (2017)

## Built on by (most cited first, 9 of 9)

- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment](https://mlsanity.com/paper/2206.08614/) (2022)
- [UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment](https://mlsanity.com/paper/2406.01069/) (2024)
- [AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models](https://mlsanity.com/paper/2508.02151/) (2025)
- [CLIP Brings Better Features to Visual Aesthetics Learners](https://mlsanity.com/paper/2307.15640/) (2023)
- [Multi-task convolutional neural network for image aesthetic assessment](https://mlsanity.com/paper/2305.09373/) (2023)
- [Unveiling The Factors of Aesthetic Preferences with Explainable AI](https://mlsanity.com/paper/2311.14410/) (2023)
- [UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings](https://mlsanity.com/paper/2605.17356/) (2026)
- [Aes3D: Aesthetic Assessment in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.05155/) (2026)

## Abstract (arXiv)

The aesthetic quality of an image is defined as the measure or appreciation of the beauty of an image. Aesthetics is inherently a subjective property but there are certain factors that influence it such as, the semantic content of the image, the attributes describing the artistic aspect, the photographic setup used for the shot, etc. In this paper we propose a method for the automatic prediction of the aesthetics of an image that is based on the analysis of the semantic content, the artistic style and the composition of the image. The proposed network includes: a pre-trained network for semantic features extraction (the Backbone); a Multi Layer Perceptron (MLP) network that relies on the Backbone features for the prediction of image attributes (the AttributeNet); a self-adaptive Hypernetwork that exploits the attributes prior encoded into the embedding generated by the AttributeNet to predict the parameters of the target network dedicated to aesthetic estimation (the AestheticNet). Given an image, the proposed multi-network is able to predict: style and composition attributes, and aesthetic score distribution. Results on three benchmark datasets demonstrate the effectiveness of the proposed method, while the ablation study gives a better understanding of the proposed network.