# A Survey on Image Aesthetic Assessment

arXiv: https://arxiv.org/abs/2103.11616 · HTML: https://mlsanity.com/paper/2103.11616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 108 references that are in the map.
- First posted on arXiv 22 Mar 2021.

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

- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- [Places205-VGGNet Models for Scene Recognition](https://mlsanity.com/paper/1508.01667/) (2015)
- [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](https://mlsanity.com/paper/1302.1700/) (2013)
- [The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification](https://mlsanity.com/paper/1411.7466/) (2014)
- [Brain-Inspired Deep Networks for Image Aesthetics Assessment](https://mlsanity.com/paper/1601.04155/) (2016)
- [The Beauty of Capturing Faces: Rating the Quality of Digital Portraits](https://mlsanity.com/paper/1501.07304/) (2015)
- [Discovering beautiful attributes for aesthetic image analysis](https://mlsanity.com/paper/1412.4940/) (2014)
- [Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification](https://mlsanity.com/paper/1503.02445/) (2015)
- [Deep Learning of Individual Aesthetics](https://mlsanity.com/paper/2009.12216/) (2020)
- [Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models](https://mlsanity.com/paper/1902.08546/) (2019)
- [rnn : Recurrent Library for Torch](https://mlsanity.com/paper/1511.07889/) (2015)

## Built on by (most cited first, 4 of 4)

- [AesRM: Improving Video Aesthetics with Expert-Level Feedback](https://mlsanity.com/paper/2604.28078/) (2026)
- [Beyond Labels: Information-Efficient Human-in-the-Loop Learning using Ranking and Selection Queries](https://mlsanity.com/paper/2602.15738/) (2026)
- [Detecting Cultural Differences in News Video Thumbnails via Computational Aesthetics](https://mlsanity.com/paper/2505.21912/) (2025)
- [Aes3D: Aesthetic Assessment in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.05155/) (2026)

## Abstract (arXiv)

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a review of the contemporary automatic image aesthetics assessment techniques. Many traditional hand-crafted and deep learning-based approaches are reviewed, and critical problem aspects are discussed, including why some features or models perform better than others and the limitations. A comparison of the quantitative results of different methods is also provided.