# Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels

arXiv: https://arxiv.org/abs/2312.03222 · HTML: https://mlsanity.com/paper/2312.03222/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [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)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Deep Outdoor Illumination Estimation](https://mlsanity.com/paper/1611.06403/) (2016)
- [Deep Hough Transform for Semantic Line Detection](https://mlsanity.com/paper/2003.04676/) (2020)
- [Attribute-Aware Attention Model for Fine-grained Representation Learning](https://mlsanity.com/paper/1901.00392/) (2019)
- [Learning Photography Aesthetics with Deep CNNs](https://mlsanity.com/paper/1707.03981/) (2017)
- [Camera View Adjustment Prediction for Improving Image Composition](https://mlsanity.com/paper/2104.07608/) (2021)
- [Aesthetics Assessment of Images Containing Faces](https://mlsanity.com/paper/1805.08685/) (2018)

## Built on by (most cited first, 3 of 3)

- [Exposing Blindspots: Cultural Bias Evaluation in Generative Image Models](https://mlsanity.com/paper/2510.20042/) (2025)
- [PromptFlare: Prompt-Generalized Defense via Cross-Attention Decoy in Diffusion-Based Inpainting](https://mlsanity.com/paper/2508.16217/) (2025)
- [From Concepts to Judgments: Interpretable Image Aesthetic Assessment](https://mlsanity.com/paper/2603.18108/) (2026)

## Abstract (arXiv)

Now many mobile phones embed deep-learning models for evaluation or guidance on photography. These models cannot provide detailed results like human pose scores or scene color scores because of the rare of corresponding aesthetic attribute data. However, the annotation of image aesthetic attribute scores requires experienced artists and professional photographers, which hinders the collection of large-scale fully-annotated datasets. In this paper, we propose to replace image attribute labels with feature extractors. First, a novel aesthetic attribute evaluation framework based on attribute features is proposed to predict attribute scores and overall scores. We call it the F2S (attribute features to attribute scores) model. We use networks from different tasks to provide attribute features to our F2S models. Then, we define an aesthetic attribute contribution to describe the role of aesthetic attributes throughout an image and use it with the attribute scores and the overall scores to train our F2S model. Sufficient experiments on publicly available datasets demonstrate that our F2S model achieves comparable performance with those trained on the datasets with fully-annotated aesthetic attribute score labels. Our method makes it feasible to learn meaningful attribute scores for various aesthetic attribute sets in different types of images with only overall aesthetic scores.