# Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks

arXiv: https://arxiv.org/abs/2603.03907 · HTML: https://mlsanity.com/paper/2603.03907/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [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)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method](https://mlsanity.com/paper/2303.15166/) (2023)
- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [Vision Transformers with Mixed-Resolution Tokenization](https://mlsanity.com/paper/2304.00287/) (2023)
- [Reliable and Efficient Image Cropping: A Grid Anchor based Approach](https://mlsanity.com/paper/1904.04441/) (2019)
- [Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content](https://mlsanity.com/paper/2503.02357/) (2025)
- [Link-Context Learning for Multimodal LLMs](https://mlsanity.com/paper/2308.07891/) (2023)
- [MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers](https://mlsanity.com/paper/2307.02321/) (2023)
- [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://mlsanity.com/paper/2507.19002/) (2025)
- [Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model](https://mlsanity.com/paper/2503.06141/) (2025)
- [Fine-grained Image Quality Assessment for Perceptual Image Restoration](https://mlsanity.com/paper/2508.14475/) (2025)
- [Goal Conditioned Reinforcement Learning for Photo Finishing Tuning](https://mlsanity.com/paper/2503.07300/) (2025)
- [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)
- [LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations](https://mlsanity.com/paper/2512.09271/) (2025)
- [Photo Filter Recommendation by Category-Aware Aesthetic Learning](https://mlsanity.com/paper/1608.05339/) (2016)

## Built on by (most cited first, 5 of 5)

- [Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation](https://mlsanity.com/paper/2605.19776/) (2026)
- [XPASS-Vis: A Dataset for Cross-Domain Personalized Image Aesthetic Assessment](https://mlsanity.com/paper/2606.15629/) (2026)
- [PALATE: Personalized Aesthetic Learning through Adaptive Taste Evolution for Multi-User Portrait Retouching](https://mlsanity.com/paper/2608.18622/) (2026)
- [AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability](https://mlsanity.com/paper/2608.26713/) (2026)
- [Beyond Global Realism: Virtual Try-On Evaluation and Optimization with Dimension-wise Garment Fidelity Assessment](https://mlsanity.com/paper/2608.29804/) (2026)

## Abstract (arXiv)

Image aesthetic assessment (IAA) has extensive applications in content creation, album management, and recommendation systems, etc. In such applications, it is commonly needed to pick out the most aesthetically pleasing image from a series of images with subtle aesthetic variations, a topic we refer to as fine-grained IAA. Unfortunately, state-of-the-art IAA models are typically designed for coarse-grained evaluation, where images with notable aesthetic differences are evaluated independently on an absolute scale. These models are inherently limited in discriminating fine-grained aesthetic differences. To address the dilemma, we contribute FGAesthetics, a fine-grained IAA database with 32,217 images organized into 10,028 series, which are sourced from diverse categories including Natural, AIGC, and Cropping. Annotations are collected via pairwise comparisons within each series. We also devise Series Refinement and Rank Calibration to ensure the reliability of data and labels. Based on FGAesthetics, we further propose FGAesQ, a novel IAA framework that learns discriminative aesthetic scores from relative ranks through Difference-preserved Tokenization (DiffToken), Comparative Text-assisted Alignment (CTAlign), and Rank-aware Regression (RankReg). FGAesQ enables accurate aesthetic assessment in fine-grained scenarios while still maintains competitive performance in coarse-grained evaluation. Extensive experiments and comparisons demonstrate the superiority of the proposed method.