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README.md

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+<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 &nbsp;&nbsp; <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).