# ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding

arXiv: https://arxiv.org/abs/2507.14533 · HTML: https://mlsanity.com/paper/2507.14533/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 62 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models](https://mlsanity.com/paper/2311.06783/) (2023)
- [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models](https://mlsanity.com/paper/2312.08962/) (2023)
- [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)
- [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)
- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments](https://mlsanity.com/paper/2411.08545/) (2024)
- [Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment](https://mlsanity.com/paper/2206.08614/) (2022)
- [GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity](https://mlsanity.com/paper/2407.12273/) (2024)
- [Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model](https://mlsanity.com/paper/2503.06141/) (2025)
- [Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization](https://mlsanity.com/paper/2407.07176/) (2024)
- [Impressions: Understanding Visual Semiotics and Aesthetic Impact](https://mlsanity.com/paper/2310.17887/) (2023)

## Built on by (most cited first, 30 of 41)

- [UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture](https://mlsanity.com/paper/2512.21675/) (2025)
- [GEMS: Agent-Native Multimodal Generation with Memory and Skills](https://mlsanity.com/paper/2603.28088/) (2026)
- [Factuality Matters: When Image Generation and Editing Meet Structured Visuals](https://mlsanity.com/paper/2510.05091/) (2025)
- [IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation](https://mlsanity.com/paper/2601.04498/) (2026)
- [LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model](https://mlsanity.com/paper/2604.20796/) (2026)
- [JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2605.04128/) (2026)
- [PhotoFramer: Multi-modal Image Composition Instruction](https://mlsanity.com/paper/2512.00993/) (2025)
- [Unlocking the Essence of Beauty: Advanced Aesthetic Reasoning with Relative-Absolute Policy Optimization](https://mlsanity.com/paper/2509.21871/) (2025)
- [I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models](https://mlsanity.com/paper/2512.04660/) (2025)
- [UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios](https://mlsanity.com/paper/2511.18050/) (2025)
- [Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping](https://mlsanity.com/paper/2602.23980/) (2026)
- [GenRouter: Unified Workflow Routing for Agentic Image Generation](https://mlsanity.com/paper/2608.16721/) (2026)
- [CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance](https://mlsanity.com/paper/2508.16644/) (2025)
- [Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?](https://mlsanity.com/paper/2605.12684/) (2026)
- [AesRM: Improving Video Aesthetics with Expert-Level Feedback](https://mlsanity.com/paper/2604.28078/) (2026)
- [LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes](https://mlsanity.com/paper/2609.03796/) (2026)
- [HanMoVLM: Large Vision-Language Models for Professional Artistic Painting Evaluation](https://mlsanity.com/paper/2603.10814/) (2026)
- [One Model, Two Minds: Task-Conditioned Reasoning for Unified Image Quality and Aesthetic Assessment](https://mlsanity.com/paper/2603.19779/) (2026)
- [PortraitCraft: A Benchmark for Portrait Composition Understanding and Generation](https://mlsanity.com/paper/2604.03611/) (2026)
- [Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation](https://mlsanity.com/paper/2604.16958/) (2026)
- [StableI2I: Spotting Unintended Changes in Image-to-Image Transition](https://mlsanity.com/paper/2605.04453/) (2026)
- [PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset](https://mlsanity.com/paper/2605.20147/) (2026)
- [AesFormer: Transform Everyday Photos into Beautiful Memories](https://mlsanity.com/paper/2605.22126/) (2026)
- [VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset](https://mlsanity.com/paper/2605.23518/) (2026)
- [DRM: Diffusion-based Reward Model With Step-wise Guidance](https://mlsanity.com/paper/2605.25661/) (2026)
- [ShutterMuse: Capture-Time Photography Guidance with MLLMs](https://mlsanity.com/paper/2606.25763/) (2026)
- [E3mo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment](https://mlsanity.com/paper/2608.10796/) (2026)
- [Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content](https://mlsanity.com/paper/2511.16908/) (2025)
- [On Semiotic-Grounded Interpretive Evaluation of Generative Art](https://mlsanity.com/paper/2604.08641/) (2026)
- [UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement](https://mlsanity.com/paper/2604.17850/) (2026)

## Abstract (arXiv)

The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present:(1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated image aesthetic dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public to advance the field.