# LAPIS: A novel dataset for personalized image aesthetic assessment

arXiv: https://arxiv.org/abs/2504.07670 · HTML: https://mlsanity.com/paper/2504.07670/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 10 Apr 2025.

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

- [Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature](https://mlsanity.com/paper/1505.00855/) (2015)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [ArtEmis: Affective Language for Visual Art](https://mlsanity.com/paper/2101.07396/) (2021)
- [Personalized Image Aesthetics Assessment with Rich Attributes](https://mlsanity.com/paper/2203.16754/) (2022)
- [Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method](https://mlsanity.com/paper/2303.15166/) (2023)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [USAR: an Interactive User-specific Aesthetic Ranking Framework for Images](https://mlsanity.com/paper/1805.01091/) (2018)
- [BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment](https://mlsanity.com/paper/2408.14173/) (2024)

## Built on by (most cited first, 16 of 16)

- [AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment](https://mlsanity.com/paper/2509.11620/) (2025)
- [VSF: Simple, Efficient, and Effective Negative Guidance in Few-Step Image Generation Models By Value Sign Flip](https://mlsanity.com/paper/2508.10931/) (2025)
- [CreBench: Human-Aligned Creativity Evaluation from Idea to Process to Product](https://mlsanity.com/paper/2511.13626/) (2025)
- [Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM](https://mlsanity.com/paper/2604.17233/) (2026)
- [Position: Universal Aesthetic Alignment Narrows Artistic Expression](https://mlsanity.com/paper/2512.11883/) (2025)
- [LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfer](https://mlsanity.com/paper/2512.18930/) (2025)
- [PortraitCraft: A Benchmark for Portrait Composition Understanding and Generation](https://mlsanity.com/paper/2604.03611/) (2026)
- [Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging](https://mlsanity.com/paper/2607.15752/) (2026)
- [Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement](https://mlsanity.com/paper/2607.27659/) (2026)
- [From Concepts to Judgments: Interpretable Image Aesthetic Assessment](https://mlsanity.com/paper/2603.18108/) (2026)
- [Personalizing Text-to-Image Generation to Individual Taste](https://mlsanity.com/paper/2604.07427/) (2026)
- [Efficient Personalization of Generative User Interfaces](https://mlsanity.com/paper/2604.09876/) (2026)
- [XPASS-Vis: A Dataset for Cross-Domain Personalized Image Aesthetic Assessment](https://mlsanity.com/paper/2606.15629/) (2026)
- [Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment](https://mlsanity.com/paper/2607.13941/) (2026)
- [Learning visual representations for compositional analysis of artworks and photographs](https://mlsanity.com/paper/2608.06142/) (2026)
- [Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning](https://mlsanity.com/paper/2608.06934/) (2026)

## Abstract (arXiv)

We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that is suitable for PIAA. LAPIS consists of 11,723 images and was meticulously curated in collaboration with art historians. Each image has an aesthetics score and a set of image attributes known to relate to aesthetic appreciation. Besides rich image attributes, LAPIS offers rich personal attributes of each annotator. We implemented two existing state-of-the-art PIAA models and assessed their performance on LAPIS. We assess the contribution of personal attributes and image attributes through ablation studies and find that performance deteriorates when certain personal and image attributes are removed. An analysis of failure cases reveals that both existing models make similar incorrect predictions, highlighting the need for improvements in artistic image aesthetic assessment. The LAPIS project page can be found at: https://github.com/Anne-SofieMaerten/LAPIS