| <div align="center"> |
| <a href="http://arxiv.org/abs/2607.15752"><img src="https://img.shields.io/badge/Arxiv-preprint-red"></a> |
| <a href="https://yzc-ippl.github.io/PRAC/"><img src="https://img.shields.io/badge/Homepage-green"></a> |
| <a href='https://yzc-ippl.github.io/PRAC/stargazers'><img src='https://img.shields.io/github/stars/yzc-ippl/PRAC.svg?style=social'></a> |
| </div> |
| |
| <h1 align="center">Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging</h1> |
|
|
| <div align="center"> |
| Zhichao Yang<sup>1†</sup>, |
| Tianjiao Gu<sup>1†</sup>, |
| Zhixianhe Zhang<sup>1</sup>, |
| Xiangfei Sheng<sup>1</sup>, |
| Pengfei Chen<sup>1</sup>, |
| Leida Li<sup>1,2*</sup> |
| </div> |
| |
| <div align="center"> |
| <sup>1</sup>School of Artificial Intelligence, |
| <sup>2</sup>State Key Laboratory of EMIM, Xidian University |
| </div> |
|
|
| <div align="center"> |
| <sup>†</sup>Equal contribution <sup>*</sup>Corresponding author |
| </div> |
| |
| <br> |
| |
| <div align="center"> |
| <img src="PRAC.png" width="900"/> |
| </div> |
| |
| <div style="font-family: sans-serif; margin-bottom: 2em;"> |
| <h2 style="border-bottom: 1px solid #eaecef; padding-bottom: 0.3em; margin-bottom: 1em;">News</h2> |
| <ul style="list-style-type: none; padding-left: 0;"> |
| <li style="margin-bottom: 0.8em;"> |
| <strong>[2026-10-03]</strong> ✨</span>✨</span> The <strong>Code</strong> and <strong>Pre-trained Weights</strong>, are now publicly available. |
| </li> |
| <li style="margin-bottom: 0.8em;"> |
| <strong> [2026-08-28]</strong> 🎉</span>🎉</span> Congratulations! Our paper has been accepted for an <strong>Oral Presentation</strong> at ACM MM 2026. |
| </li> |
| <li style="margin-bottom: 0.8em;"> |
| <strong>[2026-07-10]</strong> 🎉</span>🎉</span> Our paper, "Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging", has been accepted to <strong>ACMMM 2026</strong>! |
| </li> |
| </ul> |
| </div> |
| |
| ## Introduction |
| |
| This release contains the PARA implementation of PRAC: the Generic Aesthetic Predictor (GIAA), PreferSelect, PreferMerge, data loaders, and experiment configurations. Run commands from the repository root. |
| |
| ## Quick Start |
| |
| ### 1. Create the environment |
| |
| The following environment matches the released configuration. Install the PyTorch build that matches the CUDA runtime available to the target GPU; the example uses PyTorch 2.4.1 with CUDA 12.1. |
| |
| ```bash |
| conda create -n prac python=3.10 -y |
| conda activate prac |
| conda install pytorch==2.4.1 torchvision==0.19.1 pytorch-cuda=12.1 -c pytorch -c nvidia |
| pip install -r requirements.txt |
| ``` |
| |
| ### 2. Download the model and data |
| |
| Download the [mPLUG-Owl3-7B-241101 base checkpoint](https://huggingface.co/mPLUG/mPLUG-Owl3-7B-241101) and place it at `models/mPLUG-Owl3-7B`, or set `model.model_path` in the YAML files to another compatible local path. Then you can download the pretrained adapter from [Baidu Netdisk](https://pan.baidu.com/s/1pK-BDCCJLWci0tawbfYrog?pwd=3rd3) (pwd=`3rd3`). |
| |
| Obtain the PARA dataset from [this link](https://web.xidian.edu.cn/ldli/en/dataset.html) and extract the archive's directory to `data`. The loaders also need derived files. Generate them from the original annotations with: |
| |
| ```bash |
| python scripts/prepare_giaa_labels.py --source data/PARA --output data/PARA |
| ``` |
| |
| The final data layout is: |
| |
| ```text |
| data/PARA/ |
| ├── README.md # original dataset README |
| ├── imgs/<session_id>/<image_name> # original images |
| ├── annotation/ # original PARA annotations |
| ├── para_giaa_train_discretized.csv # from annotation/PARA-GiaaTrain.csv |
| ├── para_giaa_test_discretized.csv # from annotation/PARA-GiaaTest.csv |
| └── ... # other original PARA files |
| ``` |
| |
| The personalized-data loaders read the original `annotation/PARA-Images.csv` directly. User prompts are kept in the repository at `data/user_info_para.json`; the label-generation script does not copy or modify either file. |
| |
| ### 3. Run the inference demo |
| |
| After the base checkpoint and PARA images are available, the demo automatically scores the first image below `data/PARA/imgs`: |
| |
| ```bash |
| python demo_inference.py |
| ``` |
| |
| To use a particular image, a trained adapter, or a generated rationale: |
| |
| ```bash |
| python demo_inference.py \ |
| --image data/PARA/imgs/<session_id>/<image_name> \ |
| --adapter-path runs/para/giaa_adapter \ |
| --explain |
| ``` |
| |
| ## Repository Structure |
| |
| ```text |
| . |
| ├── configs/ |
| │ ├── giaa_train_config/ GIAA training configuration |
| │ ├── giaa_eval_config/ GIAA evaluation and demo configuration |
| │ ├── preferselect_config/ CCM/PDM data and model configuration |
| │ └── prefermerge_config/ LoRA-pool, FIM, and merge configuration |
| ├── data/ Dataset layout and download notes |
| ├── data_loader/ GIAA and personalized PARA data loaders |
| ├── modules/MplugOwl3ForGIAA.py mPLUG-Owl3 wrapper and LoRA utilities |
| ├── demo_inference.py Single-image distribution and score demo |
| ├── mplug3_train_giaaBackbone.py Train the generic aesthetic predictor |
| ├── mplug3_eval_giaaBackbone.py Evaluate a GIAA adapter |
| ├── preferSelect_CCM.py Compute collective controversy (CCM) |
| ├── preferSelect_PDM_fast.py Compute cached personalized deviation (PDM) |
| ├── preferSelect_weights_ablation.py |
| │ Combine CCM and PDM for all alpha values |
| ├── preferSelect.py Export ranked preference-rich samples |
| ├── preferMerge.py Train LoRA pool or run adapter merging |
| ├── preferMerge_FIM_fixed.py Compute FIM preference similarities |
| ├── preferMerge_choice_fast.py Select a relevance/diversity cohort |
| ├── utils.py Shared losses, metrics, and configuration helpers |
| ├── CITATION.cff Citation metadata |
| ├── LICENSE MIT license for the PRAC source code |
| └── docs/ Project page and reproduction guide |
| ``` |
| |
| ## Re-Training |
| |
| ### 1. Generic Aesthetic Predictor |
| |
| Edit [`configs/giaa_train_config/para.yaml`](configs/giaa_train_config/para.yaml) if the model, data, batch size, or output path differs from the defaults, then run: |
| |
| ```bash |
| python mplug3_train_giaaBackbone.py |
| ``` |
| |
| The trainer writes LoRA checkpoints below `runs/para/giaa`. Evaluate a selected adapter with: |
| |
| ```bash |
| python mplug3_eval_giaaBackbone.py |
| ``` |
| |
| ### 2. Preference-rich Sample Mining (PreferSelect) |
| |
| Run the commands below: |
| |
| ```bash |
| python preferSelect_CCM.py |
| python preferSelect_PDM_fast.py |
| python preferSelect_weights_ablation.py |
| python preferSelect.py |
| ``` |
| |
| ### 3. Aesthetically-resonant Cohort Merging (PreferMerge) |
| |
| Run the commands below: |
| |
| ```bash |
| python preferMerge_FIM_fixed.py |
| python preferMerge_choice_fast.py --top-n 6 --beta 0.5 |
| python preferMerge.py |
| ``` |
| |
| ## Citation |
| |
| If this code or paper helps your research, please cite: |
| |
| ```bibtex |
| @inproceedings{yang2026personalized, |
| title={Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging}, |
| author={Yang, Zhichao and Gu, Tianjiao and Zhang, Zhixianhe and Sheng, Xiangfei and Chen, Pengfei and Li, Leida}, |
| booktitle={Proceedings of the 34th ACM International Conference on Multimedia}, |
| year={2026} |
| } |
| ``` |
| |
| Citation metadata is also available in [`CITATION.cff`](CITATION.cff). |
| |
| ## License |
| |
| The PRAC source code is available under the [MIT License](LICENSE). |
| |
