# Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method

arXiv: https://arxiv.org/abs/2303.15166 · HTML: https://mlsanity.com/paper/2303.15166/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature](https://mlsanity.com/paper/1505.00855/) (2015)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [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)
- [Learning to Paint With Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/1903.04411/) (2019)
- [Personalized Image Aesthetics Assessment with Rich Attributes](https://mlsanity.com/paper/2203.16754/) (2022)
- [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)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment](https://mlsanity.com/paper/1704.00248/) (2017)
- [Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning](https://mlsanity.com/paper/1911.11419/) (2019)
- [DeepPainter: Painter Classification Using Deep Convolutional Autoencoders](https://mlsanity.com/paper/1711.08763/) (2017)

## Built on by (most cited first, 30 of 49)

- [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)
- [From Pixels to Prose: A Large Dataset of Dense Image Captions](https://mlsanity.com/paper/2406.10328/) (2024)
- [ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding](https://mlsanity.com/paper/2507.14533/) (2025)
- [OmniStyle: Filtering High Quality Style Transfer Data at Scale](https://mlsanity.com/paper/2505.14028/) (2025)
- [Large Multi-modality Model Assisted AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.17762/) (2024)
- [Exploring the Naturalness of AI-Generated Images](https://mlsanity.com/paper/2312.05476/) (2023)
- [HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment](https://mlsanity.com/paper/2503.23907/) (2025)
- [LAPIS: A novel dataset for personalized image aesthetic assessment](https://mlsanity.com/paper/2504.07670/) (2025)
- [UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment](https://mlsanity.com/paper/2406.01069/) (2024)
- [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)
- [Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories](https://mlsanity.com/paper/2405.02982/) (2024)
- [DiffArtist: Towards Structure and Appearance Controllable Image Stylization](https://mlsanity.com/paper/2407.15842/) (2024)
- [Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11511/) (2025)
- [Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model](https://mlsanity.com/paper/2502.15271/) (2025)
- [Language-Guided Visual Perception Disentanglement for Image Quality Assessment and Conditional Image Generation](https://mlsanity.com/paper/2503.02206/) (2025)
- [Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts](https://mlsanity.com/paper/2503.06678/) (2025)
- [Charm: The Missing Piece in ViT fine-tuning for Image Aesthetic Assessment](https://mlsanity.com/paper/2504.02522/) (2025)
- [Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks](https://mlsanity.com/paper/2603.03907/) (2026)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment](https://mlsanity.com/paper/2408.14173/) (2024)
- [Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11512/) (2025)
- [FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models](https://mlsanity.com/paper/2505.21032/) (2025)
- [RankList -- A Listwise Preference Learning Framework for Predicting Subjective Preferences](https://mlsanity.com/paper/2508.09826/) (2025)
- [Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment](https://mlsanity.com/paper/2512.23413/) (2025)
- [Q-Bench-Portrait: Benchmarking Multimodal Large Language Models on Portrait Image Quality Perception](https://mlsanity.com/paper/2601.18346/) (2026)
- [Variation-aware Flexible 3D Gaussian Editing](https://mlsanity.com/paper/2602.11638/) (2026)
- [For a semiotic AI: Bridging computer vision and visual semiotics for computational observation of large scale facial image archives](https://mlsanity.com/paper/2407.03268/) (2024)
- [Is Your Image a Good Storyteller?](https://mlsanity.com/paper/2501.01982/) (2024)

## Abstract (arXiv)

Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAID), which consists of 60,337 artistic images covering various art forms, with more than 360,000 votes from online users. We then propose a new method, SAAN (Style-specific Art Assessment Network), which can effectively extract and utilize style-specific and generic aesthetic information to evaluate artistic images. Experiments demonstrate that our proposed approach outperforms existing IAA methods on the proposed BAID dataset according to quantitative comparisons. We believe the proposed dataset and method can serve as a foundation for future AIAA works and inspire more research in this field. Dataset and code are available at: https://github.com/Dreemurr-T/BAID.git