# Personalized Image Aesthetics Assessment with Rich Attributes

arXiv: https://arxiv.org/abs/2203.16754 · HTML: https://mlsanity.com/paper/2203.16754/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)

## Built on by (most cited first, 30 of 58)

- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining](https://mlsanity.com/paper/2303.14302/) (2023)
- [AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception](https://mlsanity.com/paper/2401.08276/) (2024)
- [AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception](https://mlsanity.com/paper/2404.09624/) (2024)
- [Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method](https://mlsanity.com/paper/2303.15166/) (2023)
- [Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach](https://mlsanity.com/paper/2305.12726/) (2023)
- [RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions](https://mlsanity.com/paper/2302.09466/) (2023)
- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding](https://mlsanity.com/paper/2507.14533/) (2025)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [LAPIS: A novel dataset for personalized image aesthetic assessment](https://mlsanity.com/paper/2504.07670/) (2025)
- [Controllable Textual Inversion for Personalized Text-to-Image Generation](https://mlsanity.com/paper/2304.05265/) (2023)
- [The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers](https://mlsanity.com/paper/2509.18582/) (2025)
- [Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model](https://mlsanity.com/paper/2503.06141/) (2025)
- [AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment](https://mlsanity.com/paper/2509.11620/) (2025)
- [Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis](https://mlsanity.com/paper/2403.05125/) (2024)
- [Unlocking the Essence of Beauty: Advanced Aesthetic Reasoning with Relative-Absolute Policy Optimization](https://mlsanity.com/paper/2509.21871/) (2025)
- [DesignPref: Capturing Personal Preferences in Visual Design Generation](https://mlsanity.com/paper/2511.20513/) (2025)
- [Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization](https://mlsanity.com/paper/2407.07176/) (2024)
- [Language-Guided Visual Perception Disentanglement for Image Quality Assessment and Conditional Image Generation](https://mlsanity.com/paper/2503.02206/) (2025)
- [Charm: The Missing Piece in ViT fine-tuning for Image Aesthetic Assessment](https://mlsanity.com/paper/2504.02522/) (2025)
- [Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping](https://mlsanity.com/paper/2602.23980/) (2026)
- [CLIP Brings Better Features to Visual Aesthetics Learners](https://mlsanity.com/paper/2307.15640/) (2023)
- [Image Aesthetics Assessment via Learnable Queries](https://mlsanity.com/paper/2309.02861/) (2023)
- [PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings](https://mlsanity.com/paper/2506.00481/) (2025)
- [PhotoAgent: Exploratory Visual Aesthetic Planning with Large Vision Models](https://mlsanity.com/paper/2602.22809/) (2026)
- [ALL-E: Aesthetics-guided Low-light Image Enhancement](https://mlsanity.com/paper/2304.14610/) (2023)
- [Self-supervised Photographic Image Layout Representation Learning](https://mlsanity.com/paper/2403.03740/) (2024)
- [Modeling, Quantifying, and Predicting Subjectivity of Image Aesthetics](https://mlsanity.com/paper/2208.09666/) (2022)
- [MDIQA: Unified Image Quality Assessment for Multi-dimensional Evaluation and Restoration](https://mlsanity.com/paper/2508.16887/) (2025)

## Abstract (arXiv)

Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially the subject aspect, which can no longer meet the increasing demands of PIAA research. To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31,220 images with annotations by 438 subjects. PARA features wealthy annotations, including 9 image-oriented objective attributes and 4 human-oriented subjective attributes. In addition, desensitized subject information, such as personality traits, is also provided to support study of PIAA and user portraits. A comprehensive analysis of the annotation data is provided and statistic study indicates that the aesthetic preferences can be mirrored by proposed subjective attributes. We also propose a conditional PIAA model by utilizing subject information as conditional prior. Experimental results indicate that the conditional PIAA model can outperform the control group, which is also the first attempt to demonstrate how image aesthetics and subject characters interact to produce the intricate personalized tastes on image aesthetics. We believe the database and the associated analysis would be useful for conducting next-generation PIAA study. The project page of PARA can be found at: https://cv-datasets.institutecv.com/#/data-sets.