| Benchmark: |
AesBench |
| Short name: |
Aesbench |
| Long name: |
An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception |
| Description: |
AesBench is an expert-level benchmark designed to comprehensively evaluate the aesthetic understanding and perception capabilities of Multimodal Large Language Models (MLLMs). It features the Expert-labeled Aesthetics Perception Database (EAPD), containing 2,800 diversely-sourced images (natural, artistic, and AI-generated) annotated by professional aesthetic experts. It measures model performance across four shallow-to-deep dimensions: AesP (Aesthetic Perception) AesE (Aesthetic Empathy) AesA (Aesthetic Assessment) AesI (Aesthetic Interpretation) |
| Reference: |
Yipo Huang, Quan Yuan, Xiangfei Sheng, Zhichao Yang, Haoning Wu, Pengfei Chen, Yuzhe Yang, Leida Li, Weisi Lin,
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception.
In arXiv preprint arXiv:2401.08276,
2024.
|
| Last submitted: |
September 15, 2026
|
| Published: |
September 15, 2026 at 06:49:35 |
| Submissions: |
3 |
| Project page / code: |
https://github.com/yipoh/AesBench.git
|
| Open source: |
No |