# Learning Photography Aesthetics with Deep CNNs

arXiv: https://arxiv.org/abs/1707.03981 · HTML: https://mlsanity.com/paper/1707.03981/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Composition and Style Attributes Guided Image Aesthetic Assessment](https://mlsanity.com/paper/2111.04647/) (2021)
- [Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories](https://mlsanity.com/paper/2405.02982/) (2024)
- [Image Aesthetics Assessment Using Graph Attention Network](https://mlsanity.com/paper/2206.12869/) (2022)
- [Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04048/) (2021)
- [Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels](https://mlsanity.com/paper/2312.03222/) (2023)
- [Multi-task convolutional neural network for image aesthetic assessment](https://mlsanity.com/paper/2305.09373/) (2023)
- [Do Streetscapes Still Matter for Customer Ratings of Eating and Drinking Establishments in Car-Dependent Cities?](https://mlsanity.com/paper/2508.06513/) (2025)

## Abstract (arXiv)

Automatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eight aesthetic attributes along with the overall aesthetic score. We report near human performance in the prediction of the overall aesthetic score. To understand the internal representation of these attributes in the learned model, we also develop the visualization technique using back propagation of gradients. These visualizations highlight the important image regions for the corresponding attributes, thus providing insights about model's representation of these attributes. We showcase the diversity and complexity associated with different attributes through a qualitative analysis of the activation maps.