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LAPIS: A novel dataset for personalized image aesthetic assessment

arXiv 2504.07670 ↗First posted 10 Apr 2025cs.CVcited by 16

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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

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

8 of 44 references.

References · 8

  1. 2015Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Featurecited by 182
  2. 2016Photo Aesthetics Ranking Network with Attributes and Content Adaptationcited by 125
  3. 2021ArtEmis: Affective Language for Visual Artcited by 104
  4. 2022Personalized Image Aesthetics Assessment with Rich Attributescited by 58
  5. 2023Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Methodcited by 49
  6. 2017BAM! The Behance Artistic Media Dataset for Recognition Beyond Photographycited by 43
  7. 2018USAR: an Interactive User-specific Aesthetic Ranking Framework for Imagescited by 5
  8. 2024BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessmentcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 11.

16 papers.

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  1. 2025AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessmentcited by 11
  2. 2025VSF: Simple, Efficient, and Effective Negative Guidance in Few-Step Image Generation Models By Value Sign Flipcited by 4
  3. 2025CreBench: Human-Aligned Creativity Evaluation from Idea to Process to Productcited by 2
  4. 2026Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLMcited by 2
  5. 2025Position: Universal Aesthetic Alignment Narrows Artistic Expressioncited by 1
  6. 2025LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfercited by 1
  7. 2026PortraitCraft: A Benchmark for Portrait Composition Understanding and Generationcited by 1
  8. 2026Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Mergingcited by 1
  9. 2026Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancementcited by 1
  10. 2026From Concepts to Judgments: Interpretable Image Aesthetic Assessmentcited by 0
  11. 2026Personalizing Text-to-Image Generation to Individual Tastecited by 0
  12. 2026Efficient Personalization of Generative User Interfacescited by 0
4 more
  1. 2026XPASS-Vis: A Dataset for Cross-Domain Personalized Image Aesthetic Assessmentcited by 0
  2. 2026Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessmentcited by 0
  3. 2026Learning visual representations for compositional analysis of artworks and photographscited by 0
  4. 2026Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learningcited by 0