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Uniaa: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark

The Unified Multi-modal Image Aesthetic Assessment Framework, containing a baseline (a) and a benchmark (b). The aesthetic perception performance of UNIAA-LLaVA and other MLLMs is shown in (c).

The IAA Datasets Conversion Paradigm for UNIAA-LLaVA.

The UNIAA-Bench overview. (a) UNIAA-QA contains 5354 Image-Question-Answer samples and (b) UNIAA-Describe contains 501 Image-Description samples. (c) For open-source MLLMs, Logits can be extracted to calculate the score.

Release

  • [9/25] 🔥 Our UNIAA data is released! The corresponding fine-tuning and evaluation code can be found in the GitHub repository folder.
  • [4/15] 🔥 We build the page of UNIAA!

Performance

Aesthetic Perception Performance

Aesthetic Description Performance

Aesthetic Assessment Performance

Zero-shot

Supervised learning on AVA and TAD66K

Training on data of UNIAA

Step 1: Download Images and Json files

Step 2: Training On Specific MLLM

Test on UNIAA-Bench

For Aesthetic Perception

Step 1: Download Images and Json files

Step 2: Run the inference code

Step 3: Calculate the score

For Aesthetic Description

Step 1: Download Images and Json files

Step 2: Run the inference code

Citation

If you find UNIAA useful for your your research and applications, please cite using this BibTeX:

@misc{zhou2024uniaa,
      title={UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark}, 
      author={Zhaokun Zhou and Qiulin Wang and Bin Lin and Yiwei Su and Rui Chen and Xin Tao and Amin Zheng and Li Yuan and Pengfei Wan and Di Zhang},
      year={2024},
      eprint={2404.09619},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contact

If you have any questions, please feel free to email wangqiulin@kuaishou.com and zhouzhaokun@stu.pku.edu.cn.