# Modeling, Quantifying, and Predicting Subjectivity of Image Aesthetics

arXiv: https://arxiv.org/abs/2208.09666 · HTML: https://mlsanity.com/paper/2208.09666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Personalized Image Aesthetics Assessment with Rich Attributes](https://mlsanity.com/paper/2203.16754/) (2022)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- [Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning](https://mlsanity.com/paper/1911.11419/) (2019)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)

## Built on by (most cited first, 2 of 2)

- [From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images](https://mlsanity.com/paper/2511.22805/) (2025)
- [A3: Towards Advertising Aesthetic Assessment](https://mlsanity.com/paper/2603.24037/) (2026)

## Abstract (arXiv)

Assessing image aesthetics is a challenging computer vision task. One reason is that aesthetic preference is highly subjective and may vary significantly among people for certain images. Thus, it is important to properly model and quantify such textitsubjectivity, but there has not been much effort to resolve this issue. In this paper, we propose a novel unified probabilistic framework that can model and quantify subjective aesthetic preference based on the subjective logic. In this framework, the rating distribution is modeled as a beta distribution, from which the probabilities of being definitely pleasing, being definitely unpleasing, and being uncertain can be obtained. We use the probability of being uncertain to define an intuitive metric of subjectivity. Furthermore, we present a method to learn deep neural networks for prediction of image aesthetics, which is shown to be effective in improving the performance of subjectivity prediction via experiments. We also present an application scenario where the framework is beneficial for aesthetics-based image recommendation.